Methods for diagnosis, prognosis and monitoring of breast cancer and reagents therefor

ABSTRACT

The present disclosure provides methods and reagents for the diagnosis, prognosis or the monitoring of breast cancer, including various subtypes of breast cancer including, for example, estrogen receptor (ER) negative breast cancer, ER positive breast cancer, triple negative breast cancer (TNBC) and other subtypes of breast cancer.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is the U.S. national phase application filed under 35 U.S.C. § 371 claiming benefit to International Patent Application No. PCT/AU2015/50549, filed on Sep. 15, 2015, which is entitled to priority under 35 U.S.C. § 119(e) to Australian Provisional Patent Application No. 2014903680, filed Sep. 15, 2014, the entire disclosures of which are expressly incorporated herein by reference.

TECHNICAL FIELD

The present disclosure generally relates to methods and reagents for the diagnosis, prognosis or the monitoring of breast cancer, including various subtypes of breast cancer including, for example, estrogen receptor (ER) negative breast cancer, ER positive breast cancer, triple negative breast cancer (TNBC) and other subtypes of breast cancer.

BACKGROUND

Cancer is a leading cause of disease worldwide. Breast cancer is one of the most common forms of cancer, affecting both females and males globally. Various subtypes of breast cancer have been distinguished based on a number of factors including the histopathological type of tumor, the grade of the tumor, the stage of the tumor, and the expression of genes which are characteristic of particular subtypes of breast cancer. Determination of the particular subtype of cancer in a patient is often of critical importance in determining the most appropriate course of treatment for the patient.

ER negative (ER−ve) breast cancer and ER positive (ER+ve) breast cancer are two recognised subtypes of breast cancer, defined by the presence or absence of expression of the estrogen receptor gene. Triple negative breast cancer (TNBC) is another recognised subtype of breast cancer. The TNBC subtype is clinically defined by the absence of ER and progesterone receptor (PR) expression, and neither overexpression nor amplification of human epidermal growth factor receptor 2 (HER2). TNBC represents approximately 15-20% of all newly diagnosed breast cancer cases and is generally associated with high risk of disease recurrence and shorter overall survival compared to non-TNBC. Broadly, TNBC patients can be categorized into two distinct groups; those that succumb to their disease within 3-5 years regardless of treatment, and those that remain disease free to the extent that their overall survival exceeds that of non-TNBC patients (i.e. approximately >8 to 10 years post-diagnosis).

Currently, methods by which breast cancer patients are stratified into high- and low-risk subgroups remain limited to staging by clinicopathological factors such as tumor size, level of invasiveness and lymph node infiltration. However, unlike other breast cancer subtypes, TNBC outcome is less closely related to stage. Thus, there is a need to identify a robust method by which TNBC patients can be stratified to enable more informed disease management.

Previous efforts to stratify early breast cancer prognosis have primarily focused on multi-gene expression signatures. In addition to multi-gene expression assays, DNA methylation signatures are being assessed as potential molecular biomarkers of cancer. Despite growing interest in the prognostic significance of DNA methylation in breast cancer, there have been no studies specifically investigating the DNA methylation profile of human breast cancer or human breast cancer subtypes and its association with disease outcome.

There is a need in the art for improved methods for the diagnosis of breast cancer, as well as for the diagnosis of specific subtypes of breast cancer e.g., ER−ve breast cancer, ER+ve breast cancer and TNBC. There is also a need for methods of prognosis, including predicting the likelihood of patient survival for, patients diagnosed with breast cancer.

SUMMARY

The present inventors performed a genome-wide DNA methylation profiling analysis on CpG rich DNA from a number of breast cancer samples. In doing so, the inventors identified novel regions of differential methylation containing one or more CpG dinucleotides, including regional methylation profiles that are specific to breast cancer cells in comparison to healthy cells. The inventors also identified regions of differential methylation which were specific to various subtypes of breast cancer, including ER−ve breast cancer and TNBC. Specific regions of differential methylation were validated for particular subtypes of breast cancer using the cancer genome atlas (TCGA) methylation data. The inventors identified at least 822 hypermethylated and at least 43 hypomethylated, statistically significant, differentially methylated regions (DMRs) harboring 64,005 and 623 CpG sites respectively. Of these, a number of DMRs were shown to be characteristic of particular subtypes of breast cancer, including ER−ve breast cancer and TNBC. These markers have been demonstrated to have significant value in the diagnosis and prognosis of breast cancer, including in the diagnosis and prognosis of ER−ve breast cancer and/or of TNBC.

For example, the inventors identified at least 36 DMRs which were shown to be specific to TNBC samples and which therefore provide significant utility in the diagnosis of TNBC. In addition, amongst these TNBC-specific DMRs, three clusters of DMRs were shown to be reliably predictive of greater or lesser survival outcomes. These DMRs therefore also provide significant utility in the stratification of severity of TNBC during prognosis. Furthermore, a particular subset of CpG sites in any one or more DMRs selected from within a group of 17 particular DMRs have been shown by the inventors to be associated with strong survival outcomes. Therefore, any one or more CpG sites within this subset of DMRs, in any combination, can be used to determine the likelihood of survival of a subject having TNBC.

Particular examples of genes and promoters associated with the DMRs identified in the present disclosure include the WT1 and WT1 antisense (WT1-AS) gene and its bidirectional promoter. Determining the methylation status associated with any one or more of the specific genes and associated promoters disclosed herein (such as the WT1 and WT1-AS genes) is particularly useful in the diagnosis and prognosis of breast cancer, and of particular subtypes of breast cancer such as, for example, ER−ve breast cancer, ER+ve breast cancer and/or TNBC.

Accordingly, the present disclosure provides a method for the diagnosis of breast cancer in a subject, said method comprising:

-   (i) determining the methylation status of one or more CpG     dinucleotide sequences within one or more genomic regions defined in     Table 1 in a test sample obtained from the subject; and -   (ii) identifying differential methylation of said one or more CpG     dinucleotide sequences in the test sample relative to a reference     level of methylation for the corresponding one or more CpG     dinucleotide sequences,     wherein differential methylation of said one or more CpG     dinucleotide sequences in the test sample relative to the reference     level is indicative of the subject having breast cancer.

In one example increased methylation at one or more CpG dinucleotide sequences within one or more genomic regions defined in Table 1 relative to the reference level may be indicative of a subject having breast cancer; and/or decreased methylation at one or more CpG dinucleotide sequences within one or more genomic regions defined in Table 1 relative to the reference level may be indicative of a subject having breast cancer.

In another example, the identification of differential methylation of one or more CpG dinucleotide sequences within one or more genomic regions defined in Table 1 relative to the reference level is indicative of a subject having breast cancer which is characterised as being estrogen receptor negative (ER−ve) breast cancer.

Alternatively or in addition, the identification of differential methylation at one or more CpG dinucleotide sequences within one or more genomic regions defined in Table 2 and/or Table 3 relative to the reference level of methylation is indicative of the subject having Triple Negative Breast Cancer (TNBC).

The present disclosure also provides a method for prognosis of, predicting the therapeutic outcome of, and/or monitoring the progression of, Triple Negative Breast Cancer (TNBC) in a subject, said method comprising:

-   (i) determining differential methylation of one or more CpG     dinucleotide sequences within one or more genomic regions defined in     Table 3 in the subject relative to a reference level of methylation     for the corresponding one or more CpG dinucleotide sequences; and -   (ii) correlating the differential methylation determined at (i) with     a prognosis of the TNBC in the subject.

Again, in one example, differential methylation of one or more CpG dinucleotide sequences within the one or more genomic regions set forth in Table 3 relative to the reference level is associated with a likelihood of survival of the subject.

In another example, differential methylation of one or more CpG dinucleotide sequences within any one or more genomic regions defined in rows 1-14 of Table 3 relative to the reference level of methylation is associated with a decreased likelihood of survival of the subject.

In another example, differential methylation of one or more CpG dinucleotide sequences within any one or more genomic regions defined in rows 15-17 of Table 3 relative to the reference level of methylation is associated with an increased likelihood of survival of the subject.

In another example, differential methylation of one or more CpG dinucleotide sequences within any one or more genomic regions defined in rows 1-14 of Table 3 relative to the reference level of methylation is associated with a decreased likelihood of survival of the subject and differential methylation of one or more CpG dinucleotide sequences within any one or more genomic regions defined in rows 15-17 of Table 3 relative to the reference level of methylation is associated with an increased likelihood of survival of the subject.

In any of the methods disclosed herein, the differential methylation may be increased or decreased methylation relative to the reference level of methylation. In many cases, the differential methylation is increased relative to the reference level of methylation.

In one example of the methods disclosed herein, differential methylation of one or more CpG dinucleotides within the Wilms tumour protein (WT1) gene and/or its antisense counterpart, WT1-AS, is associated with a likelihood of survival of the subject. For example, increased methylation of one or more CpG dinucleotide sequences within the chr11-11623 and/or chr11-1210 genomic regions relative to a reference level of methylation of one or more CpG dinucleotide sequences within those genomic regions may be associated with a decreased likelihood of survival of the subject. In another example, increased methylation of one or more CpG dinucleotide sequences within the chr11-4047 genomic region relative to a reference level of methylation of one or more CpG dinucleotide sequences within that genomic region is associated with an increased likelihood of survival of the subject.

In any of the methods disclosed herein, the likelihood of survival may be determined in accordance with any generally accepted method of determining the likelihood of survival known in the art. In one example, the likelihood of survival is determined as a likelihood that the subject will survive at least 3 years after being diagnosed with TNBC. In another example, the likelihood of survival is determined as a likelihood that the subject will survive at least 5 years after being diagnosed with TNBC.

In any of the methods disclosed herein, methylation status of one or more CpG dinucleotide sequences may be determined according to any suitable method known in the art. For example, methylation status of one or more CpG dinucleotide sequences within the one or more genomic regions analysed may be determined by one or more techniques selected from the group consisting of a nucleic acid amplification, polymerase chain reaction (PCR), methylation specific PCR, bisulfite pyrosequencing, single-strand conformation polymorphism (SSCP) analysis, restriction analysis, microarray technology, and proteomics. For example, methylation status of one or more CpG dinucleotide sequences within the one or more genomic regions in the test sample may be determined by one or more of the following:

-   (i) performing methylation-sensitive endonuclease digestion of DNA     from the subject; -   (ii) treating nucleic acid from the subject with an amount of a     compound that selectively mutates non-methylated cytosine residues     in nucleic acid under conditions sufficient to induce mutagenesis     thereof and produce a mutant nucleic acid and amplifying the mutant     nucleic acid using at least one primer that selectively hybridizes     to the mutant nucleic acid; -   (iii) treating nucleic acid from the subject with an amount of a     compound that selectively mutates non-methylated cytosine residues     in nucleic acid under conditions sufficient to induce mutagenesis     thereof and produce a mutant nucleic acid, hybridizing a nucleic     acid probe or primer capable of specifically hybridizing to the     mutant nucleic acid and detecting the hybridized probe or primer; -   (iv) treating nucleic acid from the subject with an amount of a     compound that selectively mutates non-methylated cytosine residues     in nucleic acid under conditions sufficient to induce mutagenesis     thereof and produce a mutant nucleic acid, amplifying the mutant     nucleic acid with promoter-tagged primers, transcribing the mutant     nucleic acid in vitro to produce a transcript, subjecting the     transcript to an enzymatic base-specific cleavage, and determining     differences in mass and/or size of any cleaved fragments resulting     from mutated cysteine residues, such as by MALDI-TOF mass     spectrometry; and -   (v) treating nucleic acid from the subject with an amount of a     compound that selectively mutates non-methylated cytosine residues     in nucleic acid under conditions sufficient to induce mutagenesis     thereof, thereby producing a mutant nucleic acid, and determining     the nucleotide sequence of the mutant nucleic acid.

The compound that selectively mutates non-methylated cytosine residues may be any compound suitable for that purpose, including, for example, a salt of bisulphite.

The methods disclosed herein may be performed on any test sample taken from a subject. For example, the methylation status of one or more CpG dinucleotides sequence within the one or more genomic regions can be determined in a test sample from the subject comprising tissue and/or a body fluid comprising, or suspected of comprising, a breast cancer cell or components of a breast cancer cell. The sample may comprise tissue, a cell and/or an extract thereof taken from a breast or lymph node. When the sample comprises a body fluid, the body fluid may be selected from the group consisting of whole blood, a fraction of blood such as blood serum or plasma, urine, saliva, breast milk, pleural fluid, sweat, tears and mixtures thereof.

In any of the methods disclosed herein, the reference level of methylation may be a level of methylation determined for one or more CpG dinucleotide sequences within a corresponding genomic region of a sample selected from the group consisting of:

-   (i) a sample from a normal or healthy tissue; -   (ii) a sample comprising a non-cancerous cell; -   (iii) a sample comprising a cancerous cell other than a TNBC cell; -   (iv) an extract of any one of (i) to (iii); -   (v) a data set comprising levels of methylation for the one or more     CpG dinucleotide sequences within the corresponding genomic region     in a normal or healthy individual or a population of normal or     healthy individuals; -   (vi) a data set comprising levels of methylation for the one or more     CpG dinucleotide sequences within the corresponding genomic region     in an individual or a population of individuals having cancer of a     non-TNBC subtype; and -   (vii) a data set comprising levels of methylation for the one or     more CpG dinucleotide sequences within the corresponding genomic     region in the subject being tested wherein the levels of methylation     are determined for a matched sample having normal cells.

In one example, the reference level of methylation may be a level of methylation determined for one or more CpG dinucleotide sequences within a corresponding genomic region of a healthy breast epithelial cell. Thus, the normal or healthy tissue may comprise a breast epithelial cell. In addition, the “non-cancerous cell” may be a breast epithelial cell.

The present disclosure also provides a kit for diagnosing breast cancer in a subject, said kit comprising:

-   (i) one or more reagents to determine the methylation status of one     or more CpG dinucleotide sequences within one or more genomic     regions defined in Table 1 in a test sample obtained from the     subject; and -   (ii) a reference material which provides a reference level of     methylation of the corresponding one or more CpG dinucleotide     sequences.

The present disclosure also provides a kit for diagnosing estrogen receptor negative (ER−ve) breast cancer in a subject, said kit comprising:

-   (i) one or more reagents to determine methylation status of one or     more CpG dinucleotide sequences within one or more genomic regions     defined in Table 1 in a test sample obtained from the subject; and -   (ii) a reference material which provides a reference level of     methylation of the corresponding one or more CpG dinucleotide     sequences.

The present disclosure also provides a kit for diagnosing Triple Negative Breast Cancer (TNBC) in a subject, said kit comprising:

-   (i) one or more reagents to determine methylation status of one or     more CpG dinucleotide sequences within one or more genomic regions     defined in Table 2 and/or Table 3 in a test sample obtained from the     subject; and -   (ii) a reference material which provides a reference level of     methylation of the corresponding one or more CpG dinucleotide     sequences.

The present disclosure also provides a kit for prognosis of, predicting the therapeutic outcome of, and/or monitoring the progression of, Triple Negative Breast Cancer (TNBC) in a subject; said kit comprising:

-   (i) one or more reagents to determine methylation status of one or     more CpG dinucleotide sequences within one or more genomic regions     defined in Table 2 and/or Table 3 in a test sample obtained from the     subject; and -   (ii) a reference material which provides a reference level of     methylation of the corresponding one or more CpG dinucleotide     sequences.

In any of the kits disclosed herein, the reference level of methylation may be a level of methylation determined for one or more CpG dinucleotide sequences within a corresponding genomic region of a sample selected from the group consisting of:

-   (i) a sample from a normal or healthy tissue; -   (ii) a sample comprising a non-cancerous cell; -   (iii) a sample comprising a cancerous cell other than a TNBC cell; -   (iv) an extract of any one of (i) to (iii); -   (v) a data set comprising levels of methylation for the one or more     CpG dinucleotide sequences within the corresponding genomic region     in a normal or healthy individual or a population of normal or     healthy individuals; -   (vi) a data set comprising levels of methylation for the one or more     CpG dinucleotide sequences within the corresponding genomic region     in an individual or a population of individuals having cancer of a     non-TNBC type; and -   (vii) a data set comprising levels of methylation for the one or     more CpG dinucleotide sequences within the corresponding genomic     region in the subject being tested wherein the levels of methylation     are determined for a matched sample having normal cells.

In one example, the reference level of methylation may be a level of methylation determined for one or more CpG dinucleotide sequences within a corresponding genomic region of a healthy breast epithelial cell. Thus, the normal or healthy tissue may comprise a breast epithelial cell. In addition, the “non-cancerous cell” may be a breast epithelial cell.

The present disclosure also provides any one of the kits disclosed herein when used in any one or more of the methods disclosed herein.

In addition, the present disclosure provides the use of one or more reagents in the preparation of a medicament for diagnosing breast cancer in a subject, wherein the one or more reagents is/are configured to determine methylation status of one or more CpG dinucleotide sequences within one or more genomic regions defined in Table 1 in a test sample obtained from the subject.

The present disclosure also provides the use of one or more reagents in the preparation of a medicament for diagnosing estrogen receptor negative (ER−ve) breast cancer in a subject, wherein the one or more reagents is/are configured to determine methylation status of one or more CpG dinucleotide sequences within one or more genomic regions defined in Table 1 in a test sample obtained from the subject.

The present disclosure also provides the use of one or more reagents in the preparation of a medicament for diagnosing Triple Negative Breast Cancer (TNBC) in a subject suspected of having TNBC, wherein the one or more reagents is/are configured to determine methylation status of one or more CpG dinucleotide sequences within one or more genomic regions defined in Table 2 and/or Table 3 in a test sample obtained from the subject.

The present disclosure also provides the use of one or more reagents in the preparation of a medicament for prognosis of, predicting the therapeutic outcome of, and/or monitoring the progression of Triple Negative Breast Cancer (TNBC) in a subject, wherein the one or more reagents is/are configured to determine methylation status of one or more CpG dinucleotide sequences within one or more genomic regions defined in Table 2 and/or Table 3 in a test sample obtained from the subject.

In addition, any or the methods disclosed herein may further comprise a step of administering a therapeutic treatment to a subject. For example, the determination of the presence of a particular subtype of breast cancer in a subject may lead to the administration of a particular therapeutic treatment to that subject, which therapeutic treatment is particularly tailored to that particular subtype of breast cancer. In another example, the determination of the severity and/or state of progression of a particular subtype of breast cancer in a subject may lead to the administration of a particular therapeutic treatment to that subject, which therapeutic treatment is particularly tailored to that particular level of severity or progression of that particular subtype of breast cancer.

Each feature of any particular aspect or embodiment or example of the present disclosure may be applied mutatis mutandis to any other aspect or embodiment or example of the present disclosure.

BRIEF DESCRIPTION OF DRAWINGS

The following figures form part of the present specification and are included to further demonstrate certain aspects of the present disclosure. The disclosure may be better understood by reference to one or more of these figures in combination with the detailed description of specific embodiments presented herein.

FIG. 1. MBDCap-seq identifies DMRs in a discovery cohort. (A) A heatmap showing methylation profile of 822 hypermethylated regions across a cohort of 19 tumour and 6 matched normal samples in the discovery cohort. The columns are samples and the rows are regions. The level of methylation (number of reads normalized with respect to fully methylated sample) is represented by a colour scale—blue for low levels and red for high levels of methylation. (B) A heatmap showing methylation profile of 43 hypomethylated regions across a cohort of 19 tumour and 6 matched normal samples in the discovery cohort. The columns are samples and the rows are regions. The level of methylation (number of reads normalized with respect to fully methylated sample) is represented by a colour scale—blue for low levels and red for high levels of methylation. (C) A bar plot showing location of DMRs across functional/regulatory regions of the genome—(i) CpG islands and shores, (ii) RefSeq transcripts, and (iii) Broad ChromHMM HMEC annotation. The height of the bars represents the level of enrichment measured as ratio between the number of hypermethylated (pink) or hypomethylated (blue) regions overlapping a functional element to the expected number of such regions. Statistically significant enrichments (p-value<0.05; hyper-geometric test) are marked with asterisks. (D) Sequenom validation of five hypermethylated regions—FERD3L, C9orf125, HMX2, NPY and SATB2—is shown for an independent cohort of TNBC samples. For each region average methylation levels are shown in grey/blue for normal/tumour samples. (E) Sequenom validation of five hypermethylated regions—FERD3L, C9orf125, HMX2, NPY and SATB2—is shown for a panel of breast cancer cell lines. For each region average methylation levels are shown in grey/blue for normal/tumour cell lines. (F) A bar plot showing enrichment of genes with promoter hypermethylation in sets of genes that are up-/down-regulated in the TCGA cohort of TNBC tumour as compared to matched normal samples. The height of the bars represents the level of enrichment measured as a ratio between the observed number of up-/down-regulated genes with promoter hypermethylation to the expected number of such genes. (G) A Venn diagram showing overlap between genes with promoter hypermethylation, genes down-regulated in TCGA TNBC cohort and genes with two or more mutation in TCGA breast cancer cohort.

FIG. 2. Methylation profile of candidate DMRs in the TCGA breast cancer cohort. (A) A heatmap showing methylation profile of TCGA breast cancer samples across 4,987 HM450K probes overlapping hypermethylated DMRs identified in the discovery cohort. Rows are probes and columns are TCGA breast cancer samples profiled on HM450K—83 normal, 105 ER−VE tumour, and 354 ER+VE tumour samples. (B) Box plots showing distribution of methylation levels for two adjacent regions on chromosome 19 in TCGA normal, TNBC tumour, and other breast tumour samples. These two regions span the promoter of ZNF154 and the adjacent ZNF671 gene, are hypermethylated in the discovery cohort and exhibit regional TNBC specific hypermethylation in TCGA cohort, i.e. they are more heavily methylated in TNBC tumours as compared to normal and other tumour subtypes, as shown in the box plots. (C) Box plots showing distribution of expression levels of ZNF154 and ZNF671 genes in TCGA normal, TNBC tumour, and ER+VE tumour samples. The difference in expression of TNBC tumours versus ER+VE is found significant for both genes (t-test; ZNF154 mean diff. −0.51 with p-value 9.04e-10; ZNF671 mean diff. −0.85 and p-value of 5.50e-21).

FIG. 3. Methylation profile stratifies TNBC tumours into survival subgroups. (A) Unsupervised clustering with 4,987 HM450K probes overlapping hypermethylated DMRs identified in the discovery cohort separates TCGA TNBC tumours into three main clusters. The heatmap shows methylation profile of TCGA TNBC tumours and cluster dendrogram. The three clusters are color-coded in red, orange, and blue. (B) A Kaplan-Meier plot showing survival curves for the patients in the three clusters from (A). Additionally, individual regions of hypermethylation in the discovery cohort overlap with survival associated probes in the TCGA cohort; including (C) intergenic loci, (D) intragenic loci (e.g. the HOXB13 gene body) and (E) promoter associate loci (e.g. ZNF254 promoter). (F) Association with survival for three adjacent regions—chr11-11623, chr11-4047, and chr11-1210—spanning the WT1/WT1-AS locus is shown. These three regions are hypermethylated in the discovery cohort and overlap several probes showing statistically significant association with overall survival in both univariate and multivariate analyses. For each region the methylation profile of TCGA TNBC tumour and adjacent normal samples across overlapping survival probes is shown as a heatmap. The Kaplan-Meyer plots for each of the overlapping survival probes is shown as well with corresponding hazard ratios and p-values; values in parentheses correspond to multivariate analysis.

FIG. 4. Coverage of CpG sites and functional elements in the human genome by MBDCap-Seq and Illumina HumanMethylation450K platforms. (A) A table summarizing the number of functional/regulatory elements overlapping CpG sites covered by the MBDCap-Seq and HM450K platforms. For each platform two sets of numbers are shown with the second set (>=3 CpGs column) corresponding to elements overlapping 3 or more covered CpG sites. In both cases the fraction of covered elements is given in parentheses. (B) A Venn diagram showing overlap between CpG sites covered by MBDCap-Seq and HM450K platforms; for MBDCap-Seq covered CpGs are those overlapping SssI regions.

FIG. 5. Technological assessment of MBDCap-seq platform. (A) A scatter plot showing correlation between MBDCap-Seq readout (number of tags across SssI regions) generated from high quality fresh frozen tissue and archival FFPE samples for both primary tumour and lymph node metastatic tissue. (B) A scatter plot showing agreement between MBDCap-seq readout (number of tags overlapping a region normalized to the number of overlapping tags in SssI sample) and HM450K array readout (average beta value of HM450K probes overlapping a region) over 75,020 SssI regions that contain at least one HM450K probe. At these commonly interrogated loci, HM450K and MBDCap-seq readouts correlated highly (pearson's r>0.77). (C) The NPY promoter assayed by both MBDCap-Seq and HM450K array with high concordance. (D) This figure illustrates that the promoter region of ZNF826P contains few HM450K probes, however, MBDCap-Seq demonstrates that the region immediately downstream is heavily methylated in a region not assayed by HM450K. (E) This figure illustrates that MBDCap-Seq interrogates functionally important insulator elements that are missed by the HM450K array. (F) This figure illustrates that MBDCap-Seq interrogates functionally important enhancers that are missed by the HM450K array.

FIG. 6. This figure illustrates the MBDCap-Seq analysis pipeline at an example locus in 3 steps as follows: 1) HOMER peak detection software is applied to the fully methylated (Roche Blood SssI) sample to identify 230,655 peaks or regions of interest or SssI regions. These peaks are used as ground set of regions for subsequent comparative statistical analysis. 2) For each MBDCap-Seq sample to be analysed, the number of sequenced tags overlapping each region of interest is computed. This results in a table of counts where columns are samples (6 normal and 19 tumour) and rows are the 230,655 regions of interest. 3) Normal and tumour tag counts are analysed with the edgeR package to identify regions with statistically significant differences in tag distributions between normal and tumour group of samples. These regions are reported as DMRs.

FIG. 7. Technical validation of MBDCap-Seq with Sequenom methylation analysis. Eight SssI regions were selected to validate the methylation levels reported by MBDCap-Seq using Sequenom on the same DNA samples. These regions included an intergenic CpG Island and the genes NR5A2, PLD5, WT1, IRF8, IRF4, GHSR and NPBWR1. Validation data for each of these Sssi regions is represented in A-H respectively. For each region, mean CpG methylation by Sequenom (purple bars, left axis) is shown alongside MBDCap-Seq readout (number of tags overlapping the region normalized to the number of overlapping tags in SssI sample; red bars, right axis) across 2 normal and 4 tumour samples from the discovery cohort. This validation shows that regions of high methylation detected by MBDCap-Seq also show corresponding elevated cancer-specific methylation levels by Sequenom analysis, while samples with no MBDCap-Seq enrichment were unmethylated in corresponding patient DNA.

FIG. 8. MBDCap-Seq methylation profiles of candidate DMRs selected for Sequenom validation in an independent cohort of TNBC samples. The methylation profiles for genomic loci associated with HMX2, FERD31, SATB2, NPY and C9orf125 (illustrated in A-E respectively) exhibit low normal and high tumour methylation in the discovery cohort.

FIG. 9. Promoter hypermethylation affects actively expressed and functionally important genes. DAVID functional analysis of 308 genes with promoter hypermethylation against SP_PIR_KEYWORDS annotation identifies 12 significantly enriched (FDR<0.05) gene sets. Network representation with EnrichmentMap of enriched gene sets are provided in A-B, where nodes represent gene sets and edges represent overlap between gene sets. Node sizes are proportional to the number of genes and edge widths are proportional to the amount of overlap. Node colours encode various types of SP_PIR_KEYWORDS annotation—biological process (red), molecular function (salmon), post-translation modification (light green), domain (dark green), ligand (light cyan), and cellular component (dark cyan).

FIG. 10. Regions showing TNBC specific methylation in TCGA breast cancer cohort are provided in A-P. Boxplots showing distribution of methylation beta values in normal (pink), ER+VE (light blue), and TNBC (purple) samples in TCGA cohort across HM450K probes overlapping the 36 TNBC specific regions; one boxplot per region.

FIG. 11. Regions associated with overall survival in TCGA TNBC patients. For each of the 17 survival regions, regions overlapping 3 or more HM450K probes with statistically significant association with overall survival in TCGA TNBC cohort, we show: (i) genomic location of the region, (ii) heatmap showing methylation profile of survival probes in normal and tumour TNBC samples, and (iii) for each survival probe a Kaplan-Meier plot capturing survival curves for TNBC patients stratified based on median beta methylation value of the probe; hazard values and p-values are shown for both univariate and multivariate analyses (multivariate values are given in parentheses).

FIG. 12. This figure illustrates methylation versus expression for survival probes spanning WT1/WT1-AS locus. Scatter plots showing relationship between methylation and expression for survival probes in WT1 locus across TCGA TNBC normal (n=7; green triangles) and tumour (n=70; black circles) samples for which both RNA-Seq expression and HM450K methylation data is available. For each of the three survival regions in WT1/WT1-AS locus, the following is also provided: (i) genomic location of the region, and (ii) for each survival probe a Kaplan-Meier plot capturing survival curves for TNBC patients stratified based on median beta methylation value of the probe; hazard values and p-values are shown for both univariate and multivariate analyses (multivariate values are given in parentheses).

FIG. 13. This figure provides a ROC curve of the Diagnostic Methylation Signature based on methylation values for the 282 TNBC-specific probes

FIG. 14. This figure shows average methylation values for the 282 TNBC-specific probes in TCGA cohort normal tissues, TCGA cohort non-TNBC tissues, TCGA cohort TNBC tissue and NBFC cohort TNBC tissues.

FIG. 15. This figure provides a diagnostic methylation signature for the 282 TNBC-specific probes.

FIG. 16. This figure provides PLS estimates of variable Importance for representative probes in the full subset of 256 TNBC-specific probes.

FIG. 17. This figure provides a performance evaluation for the fifty-six signatures with each data point corresponding to a unique signature. Each data point is represented with respect to its corresponding model AUC on train (Train AUC) and test (Test AUC) datasets. The signatures are color-coded by the number of variables (i.e., probes) in the model. The plot also shows the performance of PLS model trained on the whole dataset as well as on 5, 6, 10, and 20 most important probes.

FIG. 18. This figure a) illustrates the distribution of methylation for each of the probes in subset 1 as detailed in Table 12, and b) provides a ROC curve and associated AUC statistics for diagnosing TNBC using probe subset 1.

FIG. 19. This figure a) illustrates the distribution of methylation for each of the probes in subset 2 as detailed in Table 12, and b) provides a ROC curve and associated AUC statistics for diagnosing TNBC using probe subset 2.

FIG. 20. This figure a) illustrates the distribution of methylation for each of the probes in subset 3 as detailed in Table 12, and b) provides a ROC curve and associated AUC statistics for diagnosing TNBC using probe subset 3.

FIG. 21. This figure a) illustrates the distribution of methylation for each of the probes in subset 4 as detailed in Table 12, and b) provides a ROC curve and associated AUC statistics for diagnosing TNBC using probe subset 4.

FIG. 22. This figure a) illustrates the distribution of methylation for each of the probes in subset 5 as detailed in Table 12, and b) provides a ROC curve and associated AUC statistics for diagnosing TNBC using probe subset 5.

FIG. 23. This figure a) illustrates the distribution of methylation for each of the probes in subset 6 as detailed in Table 12, and b) provides a ROC curve and associated AUC statistics for diagnosing TNBC using probe subset 6.

FIG. 24. This figure a) illustrates the distribution of methylation for each of the probes in subset 7 as detailed in Table 12, and b) provides a ROC curve and associated AUC statistics for diagnosing TNBC using probe subset 7.

FIG. 25. This figure a) illustrates the distribution of methylation for each of the probes in subset 8 as detailed in Table 12, and b) provides a ROC curve and associated AUC statistics for diagnosing TNBC using probe subset 8.

FIG. 26. This figure a) illustrates the distribution of methylation for each of the probes in subset 9 as detailed in Table 12, and b) provides a ROC curve and associated AUC statistics for diagnosing TNBC using probe subset 9.

FIG. 27. This figure a) illustrates the distribution of methylation for each of the probes in subset 10 as detailed in Table 12, and b) provides a ROC curve and associated AUC statistics for diagnosing TNBC using probe subset 10.

FIG. 28. This figure a) illustrates the distribution of methylation for each of the probes in subset 11 as detailed in Table 12, and b) provides a ROC curve and associated AUC statistics for diagnosing TNBC using probe subset 11.

FIG. 29. This figure a) illustrates the distribution of methylation for each of the probes in subset 12 as detailed in Table 12, and b) provides a ROC curve and associated AUC statistics for diagnosing TNBC using probe subset 12.

FIG. 30. This figure a) illustrates the distribution of methylation for each of the probes in subset 13 as detailed in Table 12, and b) provides a ROC curve and associated AUC statistics for diagnosing TNBC using probe subset 13.

FIG. 31. This figure a) illustrates the distribution of methylation for each of the probes in subset 14 as detailed in Table 12, and b) provides a ROC curve and associated AUC statistics for diagnosing TNBC using probe subset 14.

FIG. 32. This figure a) illustrates the distribution of methylation for each of the probes in subset 15 as detailed in Table 12, and b) provides a ROC curve and associated AUC statistics for diagnosing TNBC using probe subset 15.

FIG. 33. This figure a) illustrates the distribution of methylation for each of the probes in subset 16 as detailed in Table 12, and b) provides a ROC curve and associated AUC statistics for diagnosing TNBC using probe subset 16.

FIG. 34. This figure a) illustrates the distribution of methylation for each of the probes in subset 17 as detailed in Table 12, and b) provides a ROC curve and associated AUC statistics for diagnosing TNBC using probe subset 17.

FIG. 35. This figure a) illustrates the distribution of methylation for each of the probes in subset 18 as detailed in Table 12, and b) provides a ROC curve and associated AUC statistics for diagnosing TNBC using probe subset 18.

FIG. 36. This figure a) illustrates the distribution of methylation for each of the probes in subset 19 as detailed in Table 12, and b) provides a ROC curve and associated AUC statistics for diagnosing TNBC using probe subset 19.

FIG. 37. This figure a) illustrates the distribution of methylation for each of the probes in subset 20 as detailed in Table 12, and b) provides a ROC curve and associated AUC statistics for diagnosing TNBC using probe subset 20.

FIG. 38. This figure a) illustrates the distribution of methylation for each of the probes in subset 21 as detailed in Table 12, and b) provides a ROC curve and associated AUC statistics for diagnosing TNBC using probe subset 21.

FIG. 39. This figure a) illustrates the distribution of methylation for each of the probes in subset 22 as detailed in Table 12, and b) provides a ROC curve and associated AUC statistics for diagnosing TNBC using probe subset 22.

FIG. 40. This figure a) illustrates the distribution of methylation for each of the probes in subset 23 as detailed in Table 12, and b) provides a ROC curve and associated AUC statistics for diagnosing TNBC using probe subset 23.

FIG. 41. This figure a) illustrates the distribution of methylation for each of the probes in subset 24 as detailed in Table 12, and b) provides a ROC curve and associated AUC statistics for diagnosing TNBC using probe subset 24.

FIG. 42. This figure a) illustrates the distribution of methylation for each of the probes in subset 25 as detailed in Table 12, and b) provides a ROC curve and associated AUC statistics for diagnosing TNBC using probe subset 25.

FIG. 43. This figure a) illustrates the distribution of methylation for each of the probes in subset 26 as detailed in Table 12, and b) provides a ROC curve and associated AUC statistics for diagnosing TNBC using probe subset 26.

FIG. 44. This figure a) illustrates the distribution of methylation for each of the probes in subset 27 as detailed in Table 12, and b) provides a ROC curve and associated AUC statistics for diagnosing TNBC using probe subset 27.

FIG. 45. This figure a) illustrates the distribution of methylation for each of the probes in subset 28 as detailed in Table 12, and b) provides a ROC curve and associated AUC statistics for diagnosing TNBC using probe subset 28.

FIG. 46. This figure a) illustrates the distribution of methylation for each of the probes in subset 29 as detailed in Table 12, and b) provides a ROC curve and associated AUC statistics for diagnosing TNBC using probe subset 29.

FIG. 47. This figure a) illustrates the distribution of methylation for each of the probes in subset 30 as detailed in Table 12, and b) provides a ROC curve and associated AUC statistics for diagnosing TNBC using probe subset 30.

FIG. 48. This figure a) illustrates the distribution of methylation for each of the probes in subset 31 as detailed in Table 12, and b) provides a ROC curve and associated AUC statistics for diagnosing TNBC using probe subset 31.

FIG. 49. This figure a) illustrates the distribution of methylation for each of the probes in subset 32 as detailed in Table 12, and b) provides a ROC curve and associated AUC statistics for diagnosing TNBC using probe subset 32.

FIG. 50. This figure a) illustrates the distribution of methylation for each of the probes in subset 33 as detailed in Table 12, and b) provides a ROC curve and associated AUC statistics for diagnosing TNBC using probe subset 33.

FIG. 51. This figure a) illustrates the distribution of methylation for each of the probes in subset 34 as detailed in Table 12, and b) provides a ROC curve and associated AUC statistics for diagnosing TNBC using probe subset 34.

FIG. 52. This figure a) illustrates the distribution of methylation for each of the probes in subset 35 as detailed in Table 12, and b) provides a ROC curve and associated AUC statistics for diagnosing TNBC using probe subset 35.

FIG. 53. This figure a) illustrates the distribution of methylation for each of the probes in subset 36 as detailed in Table 12, and b) provides a ROC curve and associated AUC statistics for diagnosing TNBC using probe subset 36.

FIG. 54. This figure a) illustrates the distribution of methylation for each of the probes in subset 37 as detailed in Table 12, and b) provides a ROC curve and associated AUC statistics for diagnosing TNBC using probe subset 37.

FIG. 55. This figure a) illustrates the distribution of methylation for each of the probes in subset 38 as detailed in Table 12, and b) provides a ROC curve and associated AUC statistics for diagnosing TNBC using probe subset 38.

FIG. 56. This figure a) illustrates the distribution of methylation for each of the probes in subset 39 as detailed in Table 12, and b) provides a ROC curve and associated AUC statistics for diagnosing TNBC using probe subset 39.

FIG. 57. This figure a) illustrates the distribution of methylation for each of the probes in subset 40 as detailed in Table 12, and b) provides a ROC curve and associated AUC statistics for diagnosing TNBC using probe subset 40.

FIG. 58. This figure a) illustrates the distribution of methylation for each of the probes in subset 41 as detailed in Table 12, and b) provides a ROC curve and associated AUC statistics for diagnosing TNBC using probe subset 41.

FIG. 59. This figure a) illustrates the distribution of methylation for each of the probes in subset 42 as detailed in Table 12, and b) provides a ROC curve and associated AUC statistics for diagnosing TNBC using probe subset 42.

FIG. 60. This figure a) illustrates the distribution of methylation for each of the probes in subset 43 as detailed in Table 12, and b) provides a ROC curve and associated AUC statistics for diagnosing TNBC using probe subset 43.

FIG. 61. This figure a) illustrates the distribution of methylation for each of the probes in subset 44 as detailed in Table 12, and b) provides a ROC curve and associated AUC statistics for diagnosing TNBC using probe subset 44.

FIG. 62. This figure a) illustrates the distribution of methylation for each of the probes in subset 45 as detailed in Table 12, and b) provides a ROC curve and associated AUC statistics for diagnosing TNBC using probe subset 45.

FIG. 63. This figure a) illustrates the distribution of methylation for each of the probes in subset 46 as detailed in Table 12, and b) provides a ROC curve and associated AUC statistics for diagnosing TNBC using probe subset 46.

FIG. 64. This figure a) illustrates the distribution of methylation for each of the probes in subset 47 as detailed in Table 12, and b) provides a ROC curve and associated AUC statistics for diagnosing TNBC using probe subset 47.

FIG. 65. This figure a) illustrates the distribution of methylation for each of the probes in subset 48 as detailed in Table 12, and b) provides a ROC curve and associated AUC statistics for diagnosing TNBC using probe subset 48.

FIG. 66. This figure a) illustrates the distribution of methylation for each of the probes in subset 49 as detailed in Table 12, and b) provides a ROC curve and associated AUC statistics for diagnosing TNBC using probe subset 49.

FIG. 67. This figure a) illustrates the distribution of methylation for each of the probes in subset 50 as detailed in Table 12, and b) provides a ROC curve and associated AUC statistics for diagnosing TNBC using probe subset 50.

FIG. 68. This figure a) illustrates the distribution of methylation for each of the probes in subset 51 as detailed in Table 12, and b) provides a ROC curve and associated AUC statistics for diagnosing TNBC using probe subset 51.

FIG. 69. This figure a) illustrates the distribution of methylation for each of the probes in subset 52 as detailed in Table 12, and b) provides a ROC curve and associated AUC statistics for diagnosing TNBC using probe subset 52.

FIG. 70. This figure a) illustrates the distribution of methylation for each of the probes in subset 53 as detailed in Table 12, and b) provides a ROC curve and associated AUC statistics for diagnosing TNBC using probe subset 53.

FIG. 71. This figure a) illustrates the distribution of methylation for each of the probes in subset 54 as detailed in Table 12, and b) provides a ROC curve and associated AUC statistics for diagnosing TNBC using probe subset 54.

FIG. 72. This figure a) illustrates the distribution of methylation for each of the probes in subset 55 as detailed in Table 12, and b) provides a ROC curve and associated AUC statistics for diagnosing TNBC using probe subset 55.

FIG. 73. This figure a) illustrates the distribution of methylation for each of the probes in subset 56 as detailed in Table 12, and b) provides a ROC curve and associated AUC statistics for diagnosing TNBC using probe subset 56.

FIG. 74. This figure a) illustrates the distribution of methylation for each of the probes in subset 57 as detailed in Table 12, and b) provides a ROC curve and associated AUC statistics for diagnosing TNBC using probe subset 57.

FIG. 75. This figure a) illustrates the distribution of methylation for each of the probes in subset 58 as detailed in Table 12, and b) provides a ROC curve and associated AUC statistics for diagnosing TNBC using probe subset 58.

FIG. 76. This figure a) shows the importance of probes in subset 59 (s detailed in Table 12) to the diagnostic methylation signature of that subset based on In-built PLS estimates, and b) provides a ROC curve and associated AUC statistics for diagnosing TNBC using probe subset 59.

FIG. 77. This figure a) shows the importance of probes in subset 60 (s detailed in Table 12) to the diagnostic methylation signature of that subset based on In-built PLS estimates, and b) provides a ROC curve and associated AUC statistics for diagnosing TNBC using probe subset 60.

FIG. 78. This figure a) shows the importance of probes in subset 60 (s detailed in Table 12) to the diagnostic methylation signature of that subset based on In-built PLS estimates, and b) provides a ROC curve and associated AUC statistics for diagnosing TNBC using probe subset 61.

FIG. 79. This figure provides a performance evaluation for 40 methylation signatures, with each data point corresponding to a unique methylation signature. Each data point is represented with respect to its corresponding model AUC on train (Train AUC) and test (Test AUC) datasets. The signatures are color-coded by the number of variables (i.e., probes) in the model. The plot also shows the performance of PLS model trained on the whole dataset as well as on 5, 6, 10, and 20 most important probes.

FIG. 80. This figure a) illustrates the distribution of methylation for each of the probes in subset 1 as detailed in Table 13, and b) provides a ROC curve and associated AUC statistics for diagnosing TNBC using probe subset 1.

FIG. 81. This figure a) illustrates the distribution of methylation for each of the probes in subset 2 as detailed in Table 13, and b) provides a ROC curve and associated AUC statistics for diagnosing TNBC using probe subset 2.

FIG. 82. This figure a) illustrates the distribution of methylation for each of the probes in subset 3 as detailed in Table 13, and b) provides a ROC curve and associated AUC statistics for diagnosing TNBC using probe subset 3.

FIG. 83. This figure a) illustrates the distribution of methylation for each of the probes in subset 4 as detailed in Table 13, and b) provides a ROC curve and associated AUC statistics for diagnosing TNBC using probe subset 4.

FIG. 84. This figure a) illustrates the distribution of methylation for each of the probes in subset 5 as detailed in Table 13, and b) provides a ROC curve and associated AUC statistics for diagnosing TNBC using probe subset 5.

FIG. 85. This figure a) illustrates the distribution of methylation for each of the probes in subset 6 as detailed in Table 13, and b) provides a ROC curve and associated AUC statistics for diagnosing TNBC using probe subset 6.

FIG. 86. This figure a) illustrates the distribution of methylation for each of the probes in subset 7 as detailed in Table 13, and b) provides a ROC curve and associated AUC statistics for diagnosing TNBC using probe subset 7.

FIG. 87. This figure a) illustrates the distribution of methylation for each of the probes in subset 8 as detailed in Table 13, and b) provides a ROC curve and associated AUC statistics for diagnosing TNBC using probe subset 8.

FIG. 88. This figure a) illustrates the distribution of methylation for each of the probes in subset 9 as detailed in Table 13, and b) provides a ROC curve and associated AUC statistics for diagnosing TNBC using probe subset 9.

FIG. 89. This figure a) illustrates the distribution of methylation for each of the probes in subset 10 as detailed in Table 13, and b) provides a ROC curve and associated AUC statistics for diagnosing TNBC using probe subset 10.

FIG. 90. This figure a) illustrates the distribution of methylation for each of the probes in subset 11 as detailed in Table 13, and b) provides a ROC curve and associated AUC statistics for diagnosing TNBC using probe subset 11.

FIG. 91. This figure a) illustrates the distribution of methylation for each of the probes in subset 12 as detailed in Table 13, and b) provides a ROC curve and associated AUC statistics for diagnosing TNBC using probe subset 12.

FIG. 92. This figure a) illustrates the distribution of methylation for each of the probes in subset 13 as detailed in Table 13, and b) provides a ROC curve and associated AUC statistics for diagnosing TNBC using probe subset 13.

FIG. 93. This figure a) illustrates the distribution of methylation for each of the probes in subset 14 as detailed in Table 13, and b) provides a ROC curve and associated AUC statistics for diagnosing TNBC using probe subset 14.

FIG. 94. This figure a) illustrates the distribution of methylation for each of the probes in subset 15 as detailed in Table 13, and b) provides a ROC curve and associated AUC statistics for diagnosing TNBC using probe subset 15.

FIG. 95. This figure a) illustrates the distribution of methylation for each of the probes in subset 16 as detailed in Table 13, and b) provides a ROC curve and associated AUC statistics for diagnosing TNBC using probe subset 16.

FIG. 96. This figure a) illustrates the distribution of methylation for each of the probes in subset 17 as detailed in Table 13, and b) provides a ROC curve and associated AUC statistics for diagnosing TNBC using probe subset 17.

FIG. 97. This figure a) illustrates the distribution of methylation for each of the probes in subset 18 as detailed in Table 13, and b) provides a ROC curve and associated AUC statistics for diagnosing TNBC using probe subset 18.

FIG. 98. This figure a) illustrates the distribution of methylation for each of the probes in subset 19 as detailed in Table 13, and b) provides a ROC curve and associated AUC statistics for diagnosing TNBC using probe subset 19.

FIG. 99. This figure a) illustrates the distribution of methylation for each of the probes in subset 20 as detailed in Table 13, and b) provides a ROC curve and associated AUC statistics for diagnosing TNBC using probe subset 20.

FIG. 100. This figure a) illustrates the distribution of methylation for each of the probes in subset 21 as detailed in Table 13, and b) provides a ROC curve and associated AUC statistics for diagnosing TNBC using probe subset 21.

FIG. 101. This figure a) illustrates the distribution of methylation for each of the probes in subset 22 as detailed in Table 13, and b) provides a ROC curve and associated AUC statistics for diagnosing TNBC using probe subset 22.

FIG. 102. This figure a) illustrates the distribution of methylation for each of the probes in subset 23 as detailed in Table 13, and b) provides a ROC curve and associated AUC statistics for diagnosing TNBC using probe subset 23.

FIG. 103. This figure a) illustrates the distribution of methylation for each of the probes in subset 24 as detailed in Table 13, and b) provides a ROC curve and associated AUC statistics for diagnosing TNBC using probe subset 24.

FIG. 104. This figure a) illustrates the distribution of methylation for each of the probes in subset 25 as detailed in Table 13, and b) provides a ROC curve and associated AUC statistics for diagnosing TNBC using probe subset 25.

FIG. 105. This figure a) illustrates the distribution of methylation for each of the probes in subset 26 as detailed in Table 13, and b) provides a ROC curve and associated AUC statistics for diagnosing TNBC using probe subset 26.

FIG. 106. This figure a) illustrates the distribution of methylation for each of the probes in subset 27 as detailed in Table 13, and b) provides a ROC curve and associated AUC statistics for diagnosing TNBC using probe subset 27.

FIG. 107. This figure a) illustrates the distribution of methylation for each of the probes in subset 28 as detailed in Table 13, and b) provides a ROC curve and associated AUC statistics for diagnosing TNBC using probe subset 28.

FIG. 108. This figure a) illustrates the distribution of methylation for each of the probes in subset 29 as detailed in Table 13, and b) provides a ROC curve and associated AUC statistics for diagnosing TNBC using probe subset 29.

FIG. 109. This figure a) illustrates the distribution of methylation for each of the probes in subset 30 as detailed in Table 13, and b) provides a ROC curve and associated AUC statistics for diagnosing TNBC using probe subset 30.

FIG. 110. This figure a) illustrates the distribution of methylation for each of the probes in subset 31 as detailed in Table 13, and b) provides a ROC curve and associated AUC statistics for diagnosing TNBC using probe subset 31.

FIG. 111. This figure a) illustrates the distribution of methylation for each of the probes in subset 32 as detailed in Table 13, and b) provides a ROC curve and associated AUC statistics for diagnosing TNBC using probe subset 32.

FIG. 112. This figure a) illustrates the distribution of methylation for each of the probes in subset 33 as detailed in Table 13, and b) provides a ROC curve and associated AUC statistics for diagnosing TNBC using probe subset 33.

FIG. 113. This figure a) illustrates the distribution of methylation for each of the probes in subset 34 as detailed in Table 13, and b) provides a ROC curve and associated AUC statistics for diagnosing TNBC using probe subset 34.

FIG. 114. This figure a) illustrates the distribution of methylation for each of the probes in subset 35 as detailed in Table 13, and b) provides a ROC curve and associated AUC statistics for diagnosing TNBC using probe subset 35.

FIG. 115. This figure a) illustrates the distribution of methylation for each of the probes in subset 36 as detailed in Table 13, and b) provides a ROC curve and associated AUC statistics for diagnosing TNBC using probe subset 36.

FIG. 116. This figure a) illustrates the distribution of methylation for each of the probes in subset 37 as detailed in Table 13, and b) provides a ROC curve and associated AUC statistics for diagnosing TNBC using probe subset 37.

FIG. 117. This figure a) illustrates the distribution of methylation for each of the probes in subset 38 as detailed in Table 13, and b) provides a ROC curve and associated AUC statistics for diagnosing TNBC using probe subset 38.

FIG. 118. This figure a) illustrates the distribution of methylation for each of the probes in subset 39 as detailed in Table 13, and b) provides a ROC curve and associated AUC statistics for diagnosing TNBC using probe subset 39.

FIG. 119. This figure a) illustrates the distribution of methylation for each of the probes in subset 40 as detailed in Table 13, and b) provides a ROC curve and associated AUC statistics for diagnosing TNBC using probe subset 40.

FIG. 120. This figure a) illustrates the distribution of methylation for each of the probes in subset 41 as detailed in Table 13, and b) provides a ROC curve and associated AUC statistics for diagnosing TNBC using probe subset 41.

FIG. 121. This figure a) illustrates the distribution of methylation for each of the probes in subset 42 as detailed in Table 13, and b) provides a ROC curve and associated AUC statistics for diagnosing TNBC using probe subset 42.

FIG. 122. This figure a) illustrates the distribution of methylation for each of the probes in subset 43 as detailed in Table 13, and b) provides a ROC curve and associated AUC statistics for diagnosing TNBC using probe subset 43.

FIG. 123. This figure a) illustrates the distribution of methylation for each of the probes in subset 44 as detailed in Table 13, and b) provides a ROC curve and associated AUC statistics for diagnosing TNBC using probe subset 44.

FIG. 124. This figure a) illustrates the distribution of methylation for each of the probes in subset 45 as detailed in Table 13, and b) provides a ROC curve and associated AUC statistics for diagnosing TNBC using probe subset 44.

FIG. 125. This figure provides a histogram of NBCF cohort follow up time (months), where shaded regions denote breast cancer specific deaths (events) and white regions denote patients alive at the time of last follow up (non-events).

FIG. 126. This figure provides a flowchart illustrating the method used to validate and filter survival regions in the NBCF cohort.

FIG. 127. This figure shows relative levels of methylation for probes in prognostic region designated chr3-1415 (as described in Table 14) for patients in good and poor prognosis groups.

FIG. 128. This figure shows relative levels of methylation for probes in prognostic region designated chr7-12819 (as described in Table 14) for patients in good and poor prognosis groups.

FIG. 129. This figure shows relative levels of methylation for probes in prognostic region designated chr11-24690 (as described in Table 14) for patients in good and poor prognosis groups.

FIG. 130. This figure shows relative levels of methylation for probes in prognostic region designated chr2-13825 (as described in Table 14) for patients in good and poor prognosis groups.

FIG. 131. This figure shows relative levels of methylation for probes in prognostic region designated chr7-28113 (as described in Table 14) for patients in good and poor prognosis groups.

FIG. 132. This figure shows relative levels of methylation for probes in prognostic region designated chr15-2568 (as described in Table 14) for patients in good and poor prognosis groups.

FIG. 133. This figure shows relative levels of methylation for probes in prognostic region designated chr20-11674 (as described in Table 14) for patients in good and poor prognosis groups.

FIG. 134. This figure shows relative levels of methylation for probes in prognostic region designated chr11-11623 (as described in Table 14) for patients in good and poor prognosis groups.

FIG. 135. This figure shows relative levels of methylation for probes in prognostic region designated chr2-26887 (as described in Table 14) for patients in good and poor prognosis groups.

FIG. 136. This figure shows relative levels of methylation for probes in prognostic region designated chr13-5199 (as described in Table 14) for patients in good and poor prognosis groups.

FIG. 137. This figure shows relative levels of methylation for probes in prognostic region designated chr13-5196 (as described in Table 14) for patients in good and poor prognosis groups.

FIG. 138. This figure shows relative levels of methylation for probes in prognostic region designated chr8-13399 (as described in Table 14) for patients in good and poor prognosis groups.

FIG. 139. This figure shows relative levels of methylation for probes in prognostic region designated chr11-18108 (as described in Table 14) for patients in good and poor prognosis groups.

FIG. 140. This figure shows relative levels of methylation for probes in prognostic region designated chr11-24196 (as described in Table 14) for patients in good and poor prognosis groups.

FIG. 141. This figure shows relative levels of methylation for probes in prognostic region designated chr13-13783 (as described in Table 14) for patients in good and poor prognosis groups.

FIG. 142. This figure shows relative levels of methylation for probes in prognostic region designated chr18-11487 (as described in Table 14) for patients in good and poor prognosis groups.

FIG. 143. This figure shows relative levels of methylation for probes in prognostic region designated chr2-4092 (as described in Table 14) for patients in good and poor prognosis groups.

FIG. 144. This figure shows relative levels of methylation for probes in prognostic region designated chr6-25261 (as described in Table 14) for patients in good and poor prognosis groups.

FIG. 145. This figure shows relative levels of methylation for probes in prognostic region designated chr11-4047 (as described in Table 14) for patients in good and poor prognosis groups.

FIG. 146. This figure shows relative levels of methylation for probes in prognostic region designated chr2-9331 (as described in Table 14) for patients in good and poor prognosis groups.

FIG. 147. This figure provides Kaplan Meier plots for each of the probes in prognostic region designated chr3-1415 (as described in Table 14) based on methylation between survival groups in the NBCF cohort. Probes within prognostic region which are statistically significant for predicting patient outcome are boxed.

FIG. 148. This figure provides Kaplan Meier plots for each of the probes in prognostic region designated chr7-12819 (as described in Table 14) based on methylation between survival groups in the NBCF cohort. Probes within prognostic region which are statistically significant for predicting patient outcome are boxed.

FIG. 149. This figure provides Kaplan Meier plots for each of the probes in prognostic region designated chr11-24690 (as described in Table 14) based on methylation between survival groups in the NBCF cohort. Probes within prognostic region which are statistically significant for predicting patient outcome are boxed.

FIG. 150. This figure provides Kaplan Meier plots for each of the probes in prognostic region designated chr2-13825 (as described in Table 14) based on methylation between survival groups in the NBCF cohort. Probes within prognostic region which are statistically significant for predicting patient outcome are boxed.

FIG. 151. This figure provides Kaplan Meier plots for each of the probes in prognostic region designated chr7-28113 (as described in Table 14) based on methylation between survival groups in the NBCF cohort. Probes within prognostic region which are statistically significant for predicting patient outcome are boxed.

FIG. 152. This figure provides Kaplan Meier plots for each of the probes in prognostic region designated chr15-2568 (as described in Table 14) based on methylation between survival groups in the NBCF cohort. Probes within prognostic region which are statistically significant for predicting patient outcome are boxed.

FIG. 153. This figure provides Kaplan Meier plots for each of the probes in prognostic region designated chr20-11674 (as described in Table 14) based on methylation between survival groups in the NBCF cohort. Probes within prognostic region which are statistically significant for predicting patient outcome are boxed.

FIG. 154. This figure provides Kaplan Meier plots for each of the probes in prognostic region designated chr11-11623 (as described in Table 14) based on methylation between survival groups in the NBCF cohort. Probes within prognostic region which are statistically significant for predicting patient outcome are boxed.

FIG. 155. This figure provides Kaplan Meier plots for each of the probes in prognostic region designated chr2-26887 (as described in Table 14) based on methylation between survival groups in the NBCF cohort. Probes within prognostic region which are statistically significant for predicting patient outcome are boxed.

FIG. 156. This figure provides Kaplan Meier plots for each of the probes in prognostic region designated chr13-5199 (as described in Table 14) based on methylation between survival groups in the NBCF cohort. Probes within prognostic region which are statistically significant for predicting patient outcome are boxed.

FIG. 157. This figure provides Kaplan Meier plots for each of the probes in prognostic region designated chr13-5196 (as described in Table 14) based on methylation between survival groups in the NBCF cohort. Probes within prognostic region which are statistically significant for predicting patient outcome are boxed.

FIG. 158. This figure provides Kaplan Meier plots for each of the probes in prognostic region designated chr8-13399 (as described in Table 14) based on methylation between survival groups in the NBCF cohort. Probes within prognostic region which are statistically significant for predicting patient outcome are boxed.

FIG. 159. This figure provides Kaplan Meier plots for each of the probes in prognostic region designated chr11-18108 (as described in Table 14) based on methylation between survival groups in the NBCF cohort. Probes within prognostic region which are statistically significant for predicting patient outcome are boxed.

FIG. 160. This figure provides Kaplan Meier plots for each of the probes in prognostic region designated chr11-24196 (as described in Table 14) based on methylation between survival groups in the NBCF cohort. Probes within prognostic region which are statistically significant for predicting patient outcome are boxed.

FIG. 161. This figure provides Kaplan Meier plots for each of the probes in prognostic region designated chr13-13783 (as described in Table 14) based on methylation between survival groups in the NBCF cohort. Probes within prognostic region which are statistically significant for predicting patient outcome are boxed.

FIG. 162. This figure provides Kaplan Meier plots for each of the probes in prognostic region designated chr18-11487 (as described in Table 14) based on methylation between survival groups in the NBCF cohort. Probes within prognostic region which are statistically significant for predicting patient outcome are boxed.

FIG. 163. This figure provides Kaplan Meier plots for each of the probes in prognostic region designated chr2-4092 (as described in Table 14) based on methylation between survival groups in the NBCF cohort. Probes within prognostic region which are statistically significant for predicting patient outcome are boxed.

FIG. 164. This figure provides Kaplan Meier plots for each of the probes in prognostic region designated chr6-25261 (as described in Table 14) based on methylation between survival groups in the NBCF cohort. Probes within prognostic region which are statistically significant for predicting patient outcome are boxed.

FIG. 165. This figure provides Kaplan Meier plots for each of the probes in prognostic region designated chr11-4047 (as described in Table 14) based on methylation between survival groups in the NBCF cohort. Probes within prognostic region which are statistically significant for predicting patient outcome are boxed.

FIG. 166. This figure provides Kaplan Meier plots for each of the probes in prognostic region designated chr2-9331 (as described in Table 14) based on methylation between survival groups in the NBCF cohort. Probes within prognostic region which are statistically significant for predicting patient outcome are boxed.

DETAILED DESCRIPTION

General

Throughout this specification, unless specifically stated otherwise or the context requires otherwise, reference to a single step, composition of matter, group of steps or group of compositions of matter shall be taken to encompass one and a plurality (i.e. one or more) of those steps, compositions of matter, groups of steps or group of compositions of matter.

As used herein, the singular forms of “a”, “and” and “the” include plural forms of these words, unless the context clearly dictates otherwise.

The term “and/or”, e.g., “X and/or Y” shall be understood to mean either “X and Y” or “X or Y” and shall be taken to provide explicit support for both meanings or for either meaning.

Throughout this specification the word “comprise”, or variations such as “comprises” or “comprising”, will be understood to imply the inclusion of a stated element, integer or step, or group of elements, integers or steps, but not the exclusion of any other element, integer or step, or group of elements, integers or steps.

Selected Definitions

As used herein, the term “diagnosis”, and variants thereof, such as, but not limited to “diagnose” or “diagnosing” shall include, but not be limited to, a primary diagnosis of a clinical state or any primary diagnosis of a clinical state. A diagnostic method described herein is also useful for assessing the remission of a subject, or monitoring disease recurrence, or tumor recurrence, such as following surgery, radiation therapy, adjuvant therapy or chemotherapy, or determining the appearance of metastases of a primary tumor. All such uses of the assays described herein are encompassed by the present disclosure.

As used herein, the term “prognosis”, and variants thereof, such as, but not limited to “prognosing” shall refer to the prediction of the likelihood that a cancer patient e.g., a breast cancer patient, will have a cancer-attributable death or that the cancer will progress to a worsening stage in the subject, such as recurrence, metastatic spread or drug resistance of the cancer.

As used herein, the term “cancer” shall be taken to include a disease that is characterized by uncontrolled growth of cells within a subject. The term “cancer” shall not be limited to cancer of a specific tissue or cell type. Those skilled in the art will be aware that as a cancer progresses, metastases occur in organs and tissues outside the site of the primary cancer. Accordingly, the term “cancer” as used herein shall be taken to include a metastasis of a cancer in addition to a primary tumor. A particularly preferred cancer in the context of the present disclosure is breast cancer.

As used herein, the term “breast cancer” shall be understood to include a disease that is characterized by uncontrolled growth of cells from breast tissue of a subject.

As used herein, the term “estrogen receptor negative (ER−ve) breast cancer” shall be understood to refer to a breast cancer which is characterised by reduced expression of the ER gene when compared to a non-cancerous sample, or an ER+ve cancerous sample, or which is characterised by a level of expression of the ER gene which is not significantly different from the level of expression of a housekeeping gene, or which is characterised by the absence of a detectable level of expression of the ER gene, or which is characterised by the absence of expression of the ER gene.

As used herein, the term “triple negative breast cancer” or “TNBC” refers to a breast cancer that is characterised as being estrogen receptor (ER) negative, progesterone receptor (PR) negative and human epidermal growth factor receptor 2 (HER-2) negative. Thus, the level of expression of each one of ER, PR and HER-2 may be reduced when compared to a non-cancerous sample, or an ER+ve, PR+ve and HER2+ve cancerous sample, or which is characterised by a level of expression of each one of ER, PR and HER-2 which is not significantly different from the level of expression of a housekeeping gene, or which is characterised by the absence of a detectable level of expression of each one of ER, PR and HER-2, or which is characterised by the absence of expression of each one of ER, PR and HER-2.

The term “tumor” as used herein, refers to all neoplastic cell growth and proliferation, whether malignant or benign, and all pre-cancerous and cancerous cells and tissues. It will also be understood that the term “tumor sample” or similar in the context of a patient having cancer refers to a sample comprising tumor material obtained from a cancer patient. The term encompasses tumor tissue samples, for example, tissue obtained by surgical resection and tissue obtained by biopsy, such as for example, a core biopsy or a fine needle biopsy. In a particular embodiment, the tumor sample is a fixed, wax-embedded tissue sample, such as a formalin-fixed, paraffin-embedded tissue sample. Additionally, the term “tumor sample” encompasses a sample comprising tumor cells obtained from sites other than the primary tumor, e.g., circulating tumor cells.

The term “test sample” as used herein is taken to mean any tissue or body fluid sample taken from a subject having or suspected of having breast cancer. The presence of breast cancer in the subject may therefore already have been determined. Thus, the methods of the present disclosure may be used to determine a particular subtype of breast cancer (such as ER−ve, ER+ve or TNBC) in a subject known to have breast cancer. Thus, the “test sample” may be a “tumor sample” as defined herein. Alternatively, the methods of the present disclosure may be used to determine the presence of breast cancer in a subject in whom the presence of breast cancer has not previously been determined.

As used herein, the term “methylation” will be understood to mean the presence of a methyl group added by the action of a DNA methyl transferase enzyme to a cytosine base or bases in a region of nucleic acid e.g. genomic DNA. Accordingly, the term, “methylation status” as used herein refers to the presence or absence of methylation in a specific nucleic acid region e.g., genomic region. In particular, the present disclosure relates to detection of methylated cytosine (5-methylcytosine). A nucleic acid sequence may comprise one or more CpG methylation sites.

As used herein, the term “differential methylation” shall be taken to mean a change in the relative amount of methylation of a nucleic acid e.g., genomic DNA, in a biological sample e.g., such as a cell or a cell extract, or a body fluid (such as blood), obtained from a subject. In one example, the term “differential methylation” is an increased level of methylation of a nucleic acid. In another example, the term “differential methylation” is a decreased level of methylation of a nucleic acid. In the present disclosure, “differential methylation” is generally determined with reference to a baseline level of methylation for a given genomic region, such as a non-cancerous sample, including a non-cancerous matched sample from a subject known to have cancer e.g., breast cancer. For example, the level of differential methylation may be at least 2% greater or less than a baseline level of methylation, for example at least 5% greater or less than a baseline level of methylation, or at least 10% greater or less than a baseline level of methylation, or at least 15% greater or less than a baseline level of methylation, or at least 20% greater or less than a baseline level of methylation, or at least 25% greater or less than a baseline level of methylation, or at least 30% greater or less than a baseline level of methylation, or at least 40% greater or less than a baseline level of methylation, or at least 50% greater or less than a baseline level of methylation, or at least 60% greater or less than a baseline level of methylation, or at least 70% greater or less than a baseline level of methylation, or at least 80% greater or less than a baseline level of methylation, or at least 90% greater or less than a baseline level of methylation. Thus, the level of differential methylation may be at least 10%, at least 15%, at least 20%, or at least 25% greater than or less than a baseline level of methylation. For example, the level of differential methylation may be at least 10%, at least 15%, at least 20%, or at least 25% greater than a baseline level of methylation.

As used herein, a “CpG dinucleotide”, “CpG methylation site” or equivalent, shall be taken to denote a cytosine linked to a guanine by a phosphodiester bond. CpG dinucleotides are targets for methylation of the cytosine residue and may reside within coding or non-coding nucleic acids. Non-coding nucleic acids are understood in the art to include introns, 5′-untranslated regions, 3′ untranslated regions, promoter regions of a genomic gene, or intergenic regions.

As used herein, a “reference level of methylation” shall be understood to mean a level of methylation detected in a corresponding nucleic acid from a normal or healthy cell or tissue or body fluid, or a data set produced using information from a normal or healthy cell or tissue or body fluid. For example, a “reference level of methylation” may be a level of methylation in a corresponding nucleic acid from:

(i) a sample comprising a non-cancerous cell;

(ii) a sample from a normal or healthy tissue;

(iii) a sample from a healthy tissue;

(iv) an extract of any one of (i) to (iii);

(v) a data set comprising measurements of methylation for a healthy individual or a population of healthy individuals;

(vi) a data set comprising measurements of methylation for a normal individual or a population of normal individuals; and

(vii) a data set comprising measurements of methylation from the subject being tested wherein the measurements are determined in a matched sample having normal cells. Preferably, the non-cancerous sample is (i) or (ii) or (v) or (vii).

In one example, the reference level of methylation may be a level of methylation determined for one or more CpG dinucleotide sequences within a corresponding genomic region of a healthy breast epithelial cell. Thus, the normal or healthy cell or tissue may comprise a breast epithelial cell. In addition, the “non-cancerous cell” may be a breast epithelial cell. The extract of the normal or healthy cell or tissue, or of the non-cancerous cell may be an extract from a breast epithelial cell.

As used herein, the term “subject” or “patient” shall be taken to mean any animal including a human, preferably a mammal. Exemplary subjects include but are not limited to humans, primates, livestock (e.g. sheep, cows, horses, donkeys, pigs), companion animals (e.g. dogs, cats), laboratory test animals (e.g. mice, rabbits, rats, guinea pigs, hamsters), captive wild animals (e.g. fox, deer). Preferably the mammal is a human or primate. More preferably the mammal is a human.

The term “survival” as used herein refers to survival of a subject having breast cancer for a particular period of time, such as at least 3 years, and preferably 5 years, from the time of diagnosis or prognosis. For example, the term “survival” may refer to survival for at least 3 years, or at least 5 years, or for at least 10 years from the time of diagnosis or prognosis. In another example, the term “survival” may refer to survival of a subject having breast cancer for a particular period of time, such as at least 3 years, or at least 5 years, or at least 10 years following surgery or other treatment associated with breast cancer.

DNA Methylation Biomarkers

The present disclosure provides methods of diagnosing breast cancer comprising detecting the methylation status of one or more CpG dinucleotides within one or more genomic regions set forth in Table 1 in a test sample taken from a subject, and determining differential methylation at said one or more CpG dinucleotides within one or more genomic regions in the test sample relative to a reference level of methylation for the corresponding one or more CpG dinucleotides within one or more corresponding genomic regions, wherein differential methylation at said one or more CpG dinucleotides within one or more genomic regions relative to the reference level of methylation is indicative of the subject having breast cancer. The genomic regions set forth in Table 1 are defined with reference to human genome assembly version 18 (“hg18”). As used herein, “hg18” refers to the March 2006 human reference sequence (NCBI Build 36.1), which was produced by the International Human Genome Sequencing Consortium. Further information about this assembly is provided under the reference NCBI36 in the NCBI Assembly database. Thus, the nucleotide sequences of each of the regions identified in Table 1 (or in any of the Tables disclosed herein) can be identified by reference to hg18, using the “start” and “end” positions described in Table 1 (or in any of the Tables disclosed herein).

The 865 genomic regions listed in Table 1 encompass 822 hypermethylated regions and 43 hypomethylated regions found to be differentially methylated between breast cancer samples and matched normal samples. For each DMR, the following information is provided:

-   (i) unique DMR identifier (Column 2); -   (ii) genomic coordinates of DMR with respect to hg18 (Columns 3-5); -   (iii) statistics from statistical analysis with the edgeR package     (Columns 6-9); -   (iii) overlap (fraction of the region overlapping the element) with     various functional elements of the human genome (Columns 10-19); and -   (iv) number of overlapping HM450K probes (Column 20).

TABLE 1 Differentially methylated regions (DMRs) associated breast cancer. CpG CpG Row DMR adj. islands: islands: No. identifier Chromosome start end logCPM logFC p-value p-value % island % shore 1 chr8-27696 8 92066776 92067185 −19.74 8.20 1.61E−24 3.72E−19 0.75 0.25 2 chr2-45413 2 279014 280443 −18.72 5.96 2.70E−21 1.50E−16 0.76 0.24 3 chr19-49971 19 34709224 34710799 −18.31 5.72 1.36E−20 5.21E−16 1.00 0.00 4 chr6-28480 6 170442408 170443259 −20.68 8.38 1.76E−20 5.80E−16 0.87 0.13 5 chr10-29209 10 134900195 134900653 −19.54 6.12 6.11E−19 1.41E−14 1.00 0.00 6 chr8-26787 8 72918132 72919628 −18.87 5.70 3.56E−18 6.32E−14 0.59 0.41 7 chr6-6517 6 30081986 30083543 −18.89 5.15 5.11E−18 7.24E−14 0.74 0.26 8 chr19-24060 19 36531199 36531967 −20.29 6.23 9.60E−18 1.23E−13 0.64 0.36 9 chr5-29013 5 1498811 1499696 −17.95 4.73 1.84E−17 2.00E−13 0.95 0.05 10 chr7-23529 7 27162128 27163713 −18.75 4.97 2.75E−17 2.64E−13 0.61 0.39 11 chr19-6795 19 42999572 43000498 −17.54 4.53 5.92E−17 5.05E−13 0.70 0.30 12 chr18-12838 18 75659376 75660313 −20.01 5.87 1.88E−16 1.24E−12 0.60 0.40 13 chr7-5134 7 133793543 133794714 −19.41 5.57 2.51E−16 1.57E−12 0.81 0.19 14 chr6-2200 6 84619506 84620709 −19.21 4.88 4.86E−16 2.73E−12 0.78 0.22 15 chr18-4037 18 73089936 73091419 −18.26 4.49 6.17E−16 3.10E−12 0.59 0.41 16 chr8-15618 8 100023066 100024159 −18.16 4.46 8.11E−16 3.90E−12 0.73 0.27 17 chr10-33100 10 11100031 11100330 −33.94 32.15 1.88E−15 8.02E−12 1.00 0.00 18 chr10-2952 10 124895497 124898794 −17.10 4.08 2.33E−15 9.59E−12 0.62 0.38 19 chr10-17539 10 134449790 134450797 −18.35 4.55 2.55E−15 1.01E−11 1.00 0.00 20 chr22-16101 22 44641414 44642542 −18.74 4.82 2.78E−15 1.09E−11 0.88 0.12 21 chr5-23446 5 3649553 3650044 −20.12 5.65 2.99E−15 1.15E−11 1.00 0.00 22 chr8-20122 8 92066088 92066577 −19.21 5.10 3.96E−15 1.45E−11 0.35 0.65 23 chr5-19157 5 179176622 179177157 −21.31 7.21 5.27E−15 1.79E−11 0.82 0.18 24 chr14-10157 14 36056075 36059339 −16.48 3.85 5.47E−15 1.83E−11 0.99 0.01 25 chr6-10828 6 26291904 26292386 −21.31 7.23 6.07E−15 1.95E−11 0.62 0.38 26 chr6-14328 6 336724 339195 −16.77 3.99 6.09E−15 1.95E−11 0.84 0.16 27 chr20-20481 20 61277034 61279412 −17.94 4.27 6.86E−15 2.17E−11 1.00 0.00 28 chr10-2772 10 71001436 71003609 −17.41 4.06 8.97E−15 2.72E−11 0.79 0.21 29 chr10-7627 10 102463030 102464122 −18.74 4.32 1.11E−14 3.32E−11 0.75 0.25 30 chr1-23869 1 58487792 58489026 −19.18 4.87 1.14E−14 3.36E−11 0.67 0.33 31 chr8-15816 8 49631324 49631776 −21.22 7.30 1.15E−14 3.37E−11 0.42 0.58 32 chr15-6733 15 87238541 87240007 −20.87 6.16 1.78E−14 4.64E−11 0.46 0.54 33 chr16-31049 16 23755027 23756125 −20.65 6.38 1.84E−14 4.71E−11 0.53 0.47 34 chr1-41132 1 154656571 154858487 −17.87 3.95 2.20E−14 5.41E−11 0.61 0.39 35 chr3-19388 3 12304623 12305653 −20.76 6.22 2.69E−14 6.46E−11 0.69 0.31 36 chr1-38247 1 2975045 2975421 −34.06 31.91 2.87E−14 6.69E−11 1.00 0.00 37 chr13-10461 13 27449406 27451115 −17.89 4.11 2.84E−14 6.69E−11 0.85 0.15 38 chr5-25225 5 1984151 1984908 −18.71 4.53 2.86E−14 6.69E−11 1.00 0.00 39 chr8-29320 8 80687184 80688436 −19.78 5.37 3.50E−14 7.84E−11 0.91 0.09 40 chr19-29086 19 34707398 34708614 −19.17 4.81 4.05E−14 8.74E−11 0.82 0.18 41 chr5-7246 5 10617612 10618776 −17.61 3.95 4.24E−14 9.06E−11 0.92 0.08 42 chr2-4402 2 233058433 233060592 −17.28 3.81 6.03E−14 1.25E−10 0.96 0.04 43 chr20-14960 20 61106610 61108291 −17.11 3.79 6.14E−14 1.26E−10 1.00 0.00 44 chr10-28383 10 102895469 102896935 −18.19 3.92 7.21E−14 1.46E−10 0.67 0.33 45 chr3-11819 3 213034 214949 −18.78 4.35 9.12E−14 1.78E−10 0.81 0.19 46 chr1-1238 1 18835319 18835779 −20.54 5.56 9.98E−14 1.93E−10 0.76 0.24 47 chr2-9305 2 119248558 119249158 −34.09 31.85 1.34E−13 2.50E−10 0.38 0.62 48 chr18-15120 18 7106229 7107659 −20.02 5.06 1.43E−13 2.64E−10 0.56 0.44 49 chr8-17019 8 93182850 93185224 −18.07 3.95 2.87E−13 4.83E−10 0.81 0.19 50 chr9-21502 9 73954005 73954767 −18.35 4.00 3.19E−13 5.22E−10 0.87 0.13 51 chr19-5700 19 49403387 49403903 −21.49 6.85 4.05E−13 6.35E−10 0.00 0.00 52 chr8-16712 8 11596852 11598305 −18.99 4.09 4.41E−13 6.74E−10 0.89 0.11 53 chr2-26540 2 192767147 192769213 −18.37 4.05 5.12E−13 7.73E−10 0.88 0.12 54 chr6-2258 6 99385561 99386755 −18.33 3.82 7.22E−13 1.04E−09 0.60 0.40 55 chr19-16607 19 21449386 21450008 −19.42 4.65 7.89E−13 1.12E−09 0.75 0.25 56 chr16-21410 16 22731943 22733047 −18.45 4.00 9.61E−13 1.33E−09 0.84 0.16 57 chr12-11186 12 112397232 112399112 −18.55 4.05 1.25E−12 1.68E−09 0.49 0.51 58 chr20-19800 20 59261736 59262126 −20.83 6.00 1.33E−12 1.77E−09 1.00 0.00 59 chr13-12381 13 42046580 42047427 −20.57 5.46 1.71E−12 2.17E−09 0.83 0.17 60 chr3-13141 3 123385072 123386437 −18.25 3.69 1.93E−12 2.39E−09 0.66 0.34 61 chr20-22169 20 24398201 24400145 −17.89 3.64 2.23E−12 2.71E−09 0.94 0.06 62 chr1-51037 1 45024287 45025019 −19.08 4.11 2.41E−12 2.87E−09 0.45 0.55 63 chr8-4616 8 111055210 111056406 −18.69 4.03 2.85E−12 3.28E−09 0.73 0.27 64 chr6-2768 6 85528696 85531584 −17.10 3.44 3.08E−12 3.52E−09 0.49 0.51 65 chr6-4563 6 134258506 134259248 −19.33 4.04 3.20E−12 3.61E−09 0.49 0.51 66 chr15-2506 15 65914030 65915830 −18.72 3.98 3.54E−12 3.93E−09 0.79 0.21 67 chr17-23838 17 53589322 53590258 −18.84 3.96 3.54E−12 3.93E−09 0.54 0.46 68 chr2-11562 2 104827192 104828396 −19.03 4.09 3.71E−12 4.08E−09 0.84 0.16 69 chr19-8158 19 11550314 11551028 −19.57 4.25 4.28E−12 4.64E−09 0.49 0.51 70 chr1-6349 1 178470101 178471969 −17.17 3.40 5.21E−12 5.58E−09 0.80 0.20 71 chr10-14823 10 23527732 23529409 −18.27 3.63 6.81E−12 6.86E−09 0.47 0.53 72 chr1-48080 1 227635870 227837414 −19.06 3.84 8.81E−12 8.50E−09 0.93 0.07 73 chr8-7644 8 38705060 38705524 −19.68 4.03 8.87E−12 8.53E−09 0.00 0.00 74 chr7-35552 7 150736751 150738166 −18.95 3.89 1.01E−11 9.51E−09 0.70 0.30 75 chr1-39600 1 238321957 238322843 −20.54 5.38 1.09E−11 1.02E−08 1.00 0.00 76 chr18-2933 18 42589839 42591235 −18.79 3.81 1.15E−11 1.06E−08 0.66 0.34 77 chr8-19164 8 140783597 140784422 −19.22 3.98 1.19E−11 1.09E−08 0.79 0.21 78 chr1-24257 1 240753295 240754308 −18.52 3.71 1.27E−11 1.14E−08 0.55 0.45 79 chr1-41522 1 119329995 119332779 −16.74 3.21 1.28E−11 1.15E−08 0.32 0.68 80 chr7-16828 7 158629179 158630121 −17.39 3.27 1.60E−11 1.40E−08 0.90 0.10 81 chr14-7869 14 56333447 56334854 −17.70 3.19 1.87E−11 1.59E−08 0.33 0.67 82 chr3-16318 3 13896785 13897673 −20.50 5.44 1.87E−11 1.59E−08 0.62 0.38 83 chr8-16193 8 145527618 145528086 −18.88 3.61 2.62E−11 2.11E−08 1.00 0.00 84 chr7-4971 7 97199969 97201481 −18.63 3.61 2.67E−11 2.15E−08 0.65 0.35 85 chr4-12473 4 134290693 134293738 −16.94 3.20 2.73E−11 2.19E−08 0.65 0.35 86 chr10-35910 10 26263333 26264042 −21.10 5.61 2.76E−11 2.20E−08 0.98 0.02 87 chr18-11204 18 2837112 2838477 −21.56 6.65 2.76E−11 2.20E−08 0.78 0.22 88 chr1-7314 1 18841892 18843502 −19.39 4.27 3.16E−11 2.46E−08 0.74 0.26 89 chr8-9271 8 11602545 11603549 −17.78 3.34 3.30E−11 2.55E−08 0.92 0.08 90 chr8-6386 8 57232090 57232767 −17.69 3.30 3.35E−11 2.58E−08 0.60 0.40 91 chr6-23296 6 133604035 133605961 −18.59 3.61 3.41E−11 2.62E−08 0.75 0.25 92 chr8-24646 8 144312844 144313143 −21.22 5.39 4.03E−11 3.01E−08 1.00 0.00 93 chr17-35528 17 21220609 21222242 −18.47 3.69 4.66E−11 3.40E−08 0.95 0.05 94 chr1-10144 1 156417196 156418247 −19.76 4.03 5.35E−11 3.81E−08 0.84 0.16 95 chr8-13897 8 37942231 37943375 −17.53 3.31 5.33E−11 3.81E−08 0.82 0.18 96 chr19-33086 19 34710844 34713600 −16.04 3.06 5.39E−11 3.82E−08 0.86 0.14 97 chr16-33997 16 3178206 3180403 −18.27 3.44 6.33E−11 4.41E−08 0.17 0.83 98 chr10-13741 10 102409068 102409766 −17.59 3.21 6.93E−11 4.77E−08 0.75 0.25 99 chr3-19594 3 127381204 127382027 −20.85 4.97 7.29E−11 4.96E−08 0.82 0.18 100 chr1-48261 1 168897582 168898339 −20.84 5.01 8.33E−11 5.60E−08 0.00 1.00 101 chr6-9415 6 28861643 28862262 −19.68 3.84 8.55E−11 5.71E−08 0.43 0.57 102 chr10-24941 10 23501946 23503503 −17.07 3.16 8.81E−11 5.84E−08 0.82 0.18 103 chr8-28093 8 55529318 55530316 −17.98 3.37 9.28E−11 6.12E−08 0.86 0.14 104 chr17-30137 17 37477869 37478517 −20.87 4.91 9.52E−11 6.26E−08 0.80 0.20 105 chr3-11864 3 44038270 44039120 −19.04 3.63 1.15E−10 7.48E−08 0.61 0.39 106 chr10-9290 10 23519886 23521695 −16.89 3.02 1.28E−10 8.13E−08 0.55 0.45 107 chr4-23510 4 1385469 1390166 −15.36 2.94 1.33E−10 8.33E−08 0.82 0.18 108 chr11-10966 11 133443978 133444518 −20.27 4.57 1.37E−10 8.52E−08 0.85 0.15 109 chr13-9865 13 94161892 94163404 −19.16 3.79 1.77E−10 1.08E−07 1.00 0.00 110 chr3-1415 3 131718242 131718973 −18.24 3.25 1.88E−10 1.14E−07 0.29 0.71 111 chr20-20088 20 57613692 57614327 −20.68 4.47 2.32E−10 1.36E−07 0.77 0.23 112 chr6-5973 6 166499687 168501552 −17.48 3.01 2.37E−10 1.38E−07 0.85 0.15 113 chr19-20474 19 7700739 7701731 −18.84 3.60 2.64E−10 1.51E−07 0.94 0.06 114 chr8-10467 8 97238897 97242187 −16.49 2.95 2.66E−10 1.51E−07 0.39 0.61 115 chr8-8934 8 145175158 145176215 −16.93 3.04 2.90E−10 1.64E−07 0.89 0.11 116 chr17-17561 17 44160443 44161169 −21.18 5.38 3.30E−10 1.85E−07 0.82 0.18 117 chr16-16055 16 31135270 31136018 −19.76 4.08 3.35E−10 1.87E−07 0.66 0.34 118 chr1-17648 1 146219219 146219619 −20.51 4.67 3.51E−10 1.95E−07 0.34 0.66 119 chr12-894 12 112393108 112395188 −17.86 3.21 3.73E−10 2.05E−07 0.86 0.14 120 chr1-39636 1 235272348 235273389 −16.58 2.99 3.79E−10 2.08E−07 0.88 0.12 121 chr10-26954 10 94823866 94825466 −17.19 2.98 3.87E−10 2.11E−07 0.86 0.14 122 chr8-13205 8 11603797 11605017 −18.28 3.22 4.01E−10 2.17E−07 0.68 0.32 123 chr1-36055 1 227633073 227635758 −15.47 2.81 4.20E−10 2.25E−07 0.72 0.28 124 chr21-7896 21 33316567 33322693 −15.77 2.85 4.23E−10 2.26E−07 0.84 0.16 125 chr8-18828 8 55541438 55542861 −16.30 2.92 4.31E−10 2.28E−07 0.88 0.12 126 chr12-11487 12 93067341 93068786 −19.69 4.11 4.53E−10 2.39E−07 0.55 0.45 127 chr1-32857 1 242147216 242147842 −21.08 5.52 4.56E−10 2.40E−07 0.80 0.20 128 chr13-7449 13 78073465 78074010 −18.12 3.17 4.72E−10 2.48E−07 0.73 0.27 129 chr16-27458 16 3160303 3182631 −17.85 3.16 4.83E−10 2.53E−07 0.39 0.61 130 chr11-27245 11 77411553 77412151 −18.91 3.38 5.61E−10 2.87E−07 0.00 0.00 131 chr7-28012 7 103755530 103757459 −19.09 3.59 6.47E−10 3.24E−07 0.61 0.39 132 chr7-40429 7 1243450 1244528 −19.27 3.62 6.61E−10 3.30E−07 1.00 0.00 133 chr20-4787 20 5244160 5246038 −18.50 3.28 7.04E−10 3.49E−07 0.82 0.18 134 chr9-27388 9 29203497 29205489 −18.33 3.13 7.09E−10 3.50E−07 0.12 0.88 135 chr19-11834 19 8562642 8563981 −19.58 3.81 7.28E−10 3.57E−07 0.84 0.16 136 chr7-25883 7 154863002 154863839 −20.46 4.68 7.78E−10 3.78E−07 0.38 0.62 137 chr1-40004 1 226312674 226315609 −16.17 2.83 7.84E−10 3.81E−07 0.19 0.81 138 chr16-16067 16 31120490 31122067 −18.87 3.21 8.02E−10 3.87E−07 0.46 0.54 139 chr11-13529 11 20646986 20847555 −20.85 4.84 8.29E−10 3.99E−07 0.70 0.30 140 chr12-17914 12 118515687 118517758 −19.03 3.43 8.80E−10 4.20E−07 0.83 0.17 141 chr19-46269 19 6541010 6542269 −19.82 3.98 9.13E−10 4.34E−07 0.58 0.42 142 chr20-2803 20 41250974 41251609 −34.54 30.96 1.08E−09 4.99E−07 1.00 0.00 143 chr19-7319 19 35408061 35408437 −20.33 4.34 1.10E−09 5.03E−07 0.71 0.29 144 chr7-11150 7 154866571 154868288 −18.31 3.26 1.14E−09 5.20E−07 0.65 0.35 145 chr1-20056 1 90948898 90949502 −20.58 4.52 1.18E−09 5.37E−07 0.49 0.51 146 chr12-28277 12 4252918 4254496 −20.06 4.25 1.23E−09 5.57E−07 0.66 0.34 147 chr6-3358 6 117697858 117699053 −18.64 3.23 1.31E−09 5.89E−07 0.62 0.38 148 chr8-3384 8 65454075 65455349 −18.61 3.13 1.33E−09 5.96E−07 0.00 1.00 149 chr11-27664 11 64749540 64750291 −18.52 3.29 1.39E−09 6.20E−07 0.27 0.73 150 chr7-24732 7 121737427 121738331 −18.41 2.97 1.41E−09 6.30E−07 0.75 0.25 151 chr19-49834 19 21985098 21985686 −20.31 4.00 1.43E−09 6.35E−07 0.43 0.57 152 chr6-17652 6 28662601 28663625 −20.58 4.56 1.62E−09 7.13E−07 0.25 0.75 153 chr1-18702 1 146242078 146242459 −20.87 4.81 1.64E−09 7.21E−07 0.34 0.66 154 chr8-27831 8 109162559 109165258 −16.74 2.74 1.67E−09 7.33E−07 0.51 0.49 155 chr1-31174 1 150754436 150755138 −20.15 4.18 1.72E−09 7.51E−07 0.42 0.58 156 chr7-39912 7 45094906 45095877 −19.46 3.83 1.78E−09 7.71E−07 0.37 0.63 157 chr8-25495 8 11591837 11593200 −19.02 3.28 1.90E−09 8.22E−07 0.37 0.63 158 chr1-32633 1 159542085 159542934 −18.60 3.32 2.05E−09 8.74E−07 0.00 0.00 159 chr22-15317 22 25383018 25383912 −19.99 3.98 2.05E−09 8.74E−07 0.61 0.39 160 chr1-24072 1 240754763 240755457 −21.78 6.19 2.12E−09 9.00E−07 0.78 0.22 161 chr16-19848 16 85098394 85099853 −17.86 2.94 2.85E−09 1.17E−06 0.63 0.37 162 chr18-13032 18 11139121 11140070 −18.40 2.99 2.97E−09 1.21E−06 0.86 0.14 163 chr9-25007 9 93752490 93752945 −22.08 5.74 2.98E−09 1.21E−06 0.89 0.11 164 chr13-14276 13 94417645 94419167 −19.32 3.47 3.34E−09 1.34E−06 0.72 0.28 165 chr4-20909 4 1390317 1391758 −16.59 2.71 3.37E−09 1.35E−06 0.98 0.02 166 chr13-8091 13 109235458 109235911 −18.83 3.34 3.43E−09 1.37E−06 1.00 0.00 167 chr8-19479 8 105547717 105548686 −16.39 2.69 3.74E−09 1.47E−06 0.69 0.31 168 chr20-7936 20 44575280 44575737 −19.69 3.81 3.86E−09 1.51E−06 0.72 0.28 169 chr1-22437 1 147404036 147405091 −17.34 2.69 3.91E−09 1.53E−06 0.81 0.19 170 chr7-14832 7 126678417 126680079 −19.29 3.54 4.00E−09 1.56E−06 0.93 0.07 171 chr8-17320 8 145895909 145896990 −17.61 2.79 4.06E−09 1.58E−06 0.64 0.36 172 chr6-12570 6 100173498 100174098 −17.52 2.80 4.26E−09 1.65E−06 0.83 0.17 173 chr1-1764 1 226718035 226719183 −17.09 2.69 4.38E−09 1.69E−06 0.94 0.06 174 chr21-5935 21 38209736 38211169 −18.41 2.87 4.62E−09 1.77E−06 0.92 0.08 175 chr1-18828 1 205909091 205910115 −17.48 2.73 4.79E−09 1.81E−06 0.44 0.56 176 chr19-47954 19 22506840 22507427 −19.42 3.23 5.53E−09 2.06E−06 0.53 0.47 177 chr20-15341 20 21634216 21635840 −17.42 2.80 5.71E−09 2.13E−06 0.91 0.09 178 chr5-17366 5 158459878 158460825 −19.90 3.80 5.80E−09 2.16E−06 0.64 0.36 179 chr3-16920 3 129690745 129692733 −20.64 4.41 6.00E−09 2.22E−06 1.00 0.00 180 chr6-6670 6 12858176 12858647 −19.16 3.39 6.11E−09 2.26E−06 0.66 0.34 181 chr19-45411 19 56535026 56535791 −20.70 3.87 6.47E−09 2.37E−06 0.93 0.07 182 chr20-11215 20 21033980 21035294 −18.86 3.03 7.13E−09 2.58E−06 0.79 0.21 183 chr1-34837 1 198270644 198272124 −18.05 2.88 7.18E−09 2.59E−06 0.31 0.69 184 chr21-8209 21 36989873 36993170 −16.22 2.61 7.36E−09 2.65E−06 0.94 0.06 185 chr5-27730 5 9597561 9598916 −18.55 3.12 7.60E−09 2.73E−06 0.90 0.10 186 chr1-30464 1 16892517 16893245 −17.29 2.65 7.64E−09 2.73E−06 1.00 0.00 187 chr15-7466 15 46957300 46958277 −21.34 5.10 7.63E−09 2.73E−06 0.58 0.42 188 chr17-12235 17 32371653 32372627 −18.57 2.96 8.12E−09 2.88E−06 1.00 0.00 189 chr3-17474 3 126558531 126559202 −18.61 3.03 8.12E−09 2.88E−06 0.84 0.16 190 chr11-7178 11 113435550 113437712 −19.88 3.58 8.17E−09 2.89E−06 0.86 0.14 191 chr7-12819 7 100732591 100733729 −17.06 2.72 8.45E−09 2.97E−06 0.00 0.00 192 chr1-33177 1 239653649 239653948 −34.68 30.67 8.64E−09 3.01E−06 0.00 1.00 193 chr6-23289 6 27408690 27409294 −34.64 30.76 8.64E−09 3.01E−06 0.00 0.00 194 chr18-12432 18 65219359 65220459 −18.00 2.93 9.24E−09 3.20E−06 0.72 0.28 195 chr11-24690 11 125278717 125279989 −17.24 2.76 9.50E−09 3.28E−06 0.25 0.75 196 chr1-20728 1 151917440 151919117 −17.61 2.83 1.00E−08 3.45E−06 0.92 0.08 197 chr3-1158 3 173648106 173650702 −15.48 2.52 1.05E−08 3.60E−06 0.64 0.36 198 chr21-5864 21 38953773 38955606 −16.80 2.68 1.05E−08 3.61E−06 0.78 0.22 199 chr6-15257 6 7672425 7674161 −20.92 4.13 1.06E−08 3.64E−06 0.55 0.45 200 chr3-11179 3 62333852 62335905 −17.27 2.69 1.09E−08 3.72E−06 0.47 0.53 201 chr2-13825 2 182253383 182254903 −17.84 2.71 1.10E−08 3.73E−06 0.00 0.52 202 chr9-17404 9 34568108 34568587 −19.30 3.43 1.17E−08 3.95E−06 0.31 0.69 203 chr7-28113 7 24290275 24291795 −17.19 2.72 1.18E−08 3.96E−06 0.88 0.12 204 chr19-10652 19 49644032 49644762 −20.07 3.62 1.20E−08 4.00E−06 0.54 0.46 205 chr8-4283 8 86537557 86538509 −17.13 2.66 1.24E−08 4.11E−06 0.45 0.55 206 chr1-32204 1 235271550 235271938 −19.40 3.47 1.43E−08 4.69E−06 0.49 0.51 207 chr5-18793 5 9599452 9600097 −19.32 3.32 1.49E−08 4.87E−06 0.41 0.59 208 chr14-18299 14 56353014 56354411 −17.12 2.65 1.53E−08 4.97E−06 0.42 0.58 209 chr7-8996 7 154951647 154953283 −17.73 2.73 1.58E−08 5.11E−06 1.00 0.00 210 chr9-7772 9 103288066 103289677 −16.49 2.58 1.62E−08 5.25E−06 0.78 0.22 211 chr19-10697 19 62309101 62309842 −18.10 2.80 1.69E−08 5.43E−06 0.58 0.42 212 chr1-25380 1 154096691 154097077 −21.27 5.14 1.71E−08 5.48E−06 0.67 0.33 213 chr8-7773 8 69026539 69027642 −18.13 2.96 1.78E−08 5.66E−06 0.33 0.67 214 chr14-18459 14 100993460 100995003 −16.86 2.59 1.94E−08 6.13E−06 1.00 0.00 215 chr6-26510 6 78228763 78231120 −16.41 2.53 1.94E−08 6.13E−06 0.79 0.21 216 chr6-16375 6 31890480 31891670 −20.55 4.02 1.95E−08 6.14E−06 0.69 0.31 217 chr10-15047 10 1768602 1770120 −20.00 3.64 2.00E−08 6.27E−06 0.81 0.19 218 chr4-2412 4 172970178 172971846 −18.71 2.93 2.02E−08 6.30E−06 0.83 0.17 219 chr8-13432 8 65455535 65456036 −18.59 2.80 2.04E−08 6.36E−06 0.00 0.00 220 chr7-4850 7 8439552 8442214 −17.13 2.61 2.15E−08 6.64E−06 0.77 0.23 221 chr7-30884 7 127459174 127459558 −21.53 4.88 2.18E−08 6.74E−06 1.00 0.00 222 chr4-7057 4 13138180 13139150 −18.70 3.12 2.19E−08 6.77E−06 0.63 0.37 223 chr19-22107 19 44446457 44447692 −16.49 2.56 2.22E−08 6.81E−06 0.71 0.29 224 chr5-23479 5 36026653 36027313 −19.55 3.27 2.25E−08 6.89E−06 0.00 0.00 225 chr6-7762 6 43384179 43384892 −21.93 5.89 2.30E−08 6.99E−06 0.34 0.66 226 chr8-14986 8 145532810 145533610 −20.04 3.87 2.39E−08 7.27E−06 0.64 0.36 227 chr2-26142 2 167858796 167859492 −20.22 4.03 2.52E−08 7.62E−06 0.36 0.64 228 chr1-8996 1 18844034 18844768 −19.84 3.44 2.53E−08 7.62E−06 0.50 0.50 229 chr10-8209 10 26543645 26547703 −16.52 2.49 2.81E−08 8.36E−06 0.75 0.25 230 chr1-17872 1 50658646 50659783 −16.87 2.57 2.89E−08 8.58E−06 1.00 0.00 231 chr15-2568 15 50873927 50875994 −18.15 2.67 2.91E−08 8.62E−06 0.24 0.76 232 chr8-10864 8 56176559 56177228 −18.46 2.89 3.06E−08 9.03E−06 1.00 0.00 233 chr8-5843 8 57520576 57521847 −15.93 2.47 3.28E−08 9.58E−06 0.92 0.08 234 chr9-16770 9 37019348 37020844 −18.38 2.88 3.44E−08 9.99E−06 0.24 0.76 235 chr15-15389 15 46725135 46725818 −21.02 4.49 3.48E−08 1.01E−05 1.00 0.00 236 chr6-23012 6 137858031 137859078 −17.58 2.62 3.68E−08 1.06E−05 0.71 0.29 237 chr3-9567 3 148561647 148561946 −20.47 3.70 3.74E−08 1.07E−05 0.00 1.00 238 chr7-38902 7 98084470 98085748 −16.42 2.51 3.73E−08 1.07E−05 0.96 0.04 239 chr17-668 17 40399881 40401028 −19.48 3.18 3.88E−08 1.10E−05 0.99 0.01 240 chr10-6509 10 102478872 102480011 −21.29 4.15 3.96E−08 1.12E−05 0.59 0.41 241 chr11-7826 11 91597095 91599952 −18.55 2.85 4.00E−08 1.13E−05 0.93 0.07 242 chr8-21750 8 38746617 38747410 −17.48 2.62 4.08E−08 1.15E−05 0.00 0.00 243 chr9-28705 9 78823050 78824812 −17.02 2.56 4.10E−08 1.15E−05 0.94 0.06 244 chr10-32739 10 25504311 25505823 −18.14 2.79 4.17E−08 1.17E−05 0.88 0.12 245 chr1-42850 1 6401196 6402156 −19.45 3.15 4.66E−08 1.29E−05 0.53 0.47 246 chr9-31429 9 36248189 36249110 −20.51 4.04 4.72E−08 1.30E−05 0.76 0.24 247 chr6-3985 6 7051996 7052718 −20.85 4.07 4.80E−08 1.32E−05 0.89 0.11 248 chr8-10122 8 65448362 65448976 −17.96 2.56 4.81E−08 1.32E−05 0.58 0.42 249 chr9-33298 9 95750508 95751903 −17.69 2.58 4.98E−08 1.36E−05 0.65 0.35 250 chr17-22392 17 44184869 44185763 −21.04 4.48 5.21E−08 1.41E−05 0.00 0.73 251 chr1-18644 1 198275935 198278813 −16.29 2.41 5.40E−08 1.46E−05 0.31 0.69 252 chr2-3750 2 137238538 137240987 −19.40 3.13 5.59E−08 1.51E−05 0.50 0.50 253 chr7-12114 7 154856923 154860744 −16.04 2.40 5.64E−08 1.52E−05 0.86 0.14 254 chr18-8329 18 68359872 68360921 −18.86 3.00 5.88E−08 1.58E−05 0.92 0.08 255 chr19-45799 19 56521875 56522920 −18.31 2.81 5.95E−08 1.59E−05 1.00 0.00 256 chr9-33335 9 95148049 95148890 −17.51 2.57 6.00E−08 1.60E−05 0.62 0.38 257 chr18-245 18 53255803 53260282 −16.04 2.42 6.14E−08 1.63E−05 0.90 0.10 258 chr8-3960 8 24868578 24871425 −16.71 2.50 6.14E−08 1.63E−05 0.48 0.52 259 chr20-4500 20 25011764 25013922 −16.64 2.44 6.45E−08 1.70E−05 0.78 0.22 260 chr10-9549 10 50272881 50273274 −21.37 4.92 6.66E−08 1.74E−05 0.71 0.29 261 chr1-4629 1 222869931 222872599 −16.52 2.41 6.92E−08 1.80E−05 0.80 0.20 262 chr8-5807 8 144232946 144234345 −18.61 2.88 6.96E−08 1.81E−05 0.00 0.00 263 chr13-8202 13 99439503 99440212 −19.12 3.00 7.35E−08 1.89E−05 0.97 0.03 264 chr1-15009 1 239586266 239586643 −22.25 5.38 7.67E−08 1.97E−05 0.00 1.00 265 chr20-11329 20 22507549 22508115 −19.98 3.09 7.80E−08 2.00E−05 0.79 0.21 266 chr8-5087 8 65445829 65446662 −17.60 2.46 7.83E−08 2.00E−05 0.00 1.00 267 chr7-40510 7 19150608 19151892 −17.52 2.51 7.87E−08 2.01E−05 0.53 0.47 268 chr3-13959 3 27747702 27748100 −19.07 2.74 8.25E−08 2.09E−05 0.00 1.00 269 chr7-24792 7 103416300 103417009 −20.27 3.87 8.81E−08 2.22E−05 0.35 0.65 270 chr8-16413 8 120720021 120720320 −21.33 4.11 9.00E−08 2.25E−05 0.00 0.00 271 chr10-23344 10 124891388 124893492 −17.17 2.45 9.46E−08 2.34E−05 0.34 0.66 272 chr11-30051 11 20647654 20649654 −19.15 2.88 9.71E−08 2.38E−05 0.38 0.62 273 chr19-8563 19 49147040 49147610 −34.79 30.44 9.79E−08 2.40E−05 0.74 0.26 274 chr3-4075 3 63238878 63240098 −19.45 3.07 1.07E−07 2.58E−05 0.18 0.82 275 chr8-29409 8 79740841 79741568 −20.78 4.18 1.07E−07 2.59E−05 0.52 0.48 276 chr1-27293 1 152741716 152742254 −16.47 2.40 1.14E−07 2.73E−05 1.00 0.00 277 chr10-16430 10 132998977 132999442 −18.48 2.72 1.14E−07 2.73E−05 0.56 0.44 278 chr14-12954 14 91859637 91860687 −18.93 2.85 1.21E−07 2.90E−05 0.79 0.21 279 chr7-37359 7 71439704 71441020 −17.17 2.49 1.25E−07 2.98E−05 0.76 0.24 280 chr20-11674 20 36786620 36791737 −15.32 2.32 1.26E−07 3.00E−05 0.81 0.19 281 chr8-2210 8 65451687 65453842 −16.49 2.39 1.28E−07 3.03E−05 0.39 0.61 282 chr10-10042 10 102870703 102871609 −18.28 2.48 1.29E−07 3.06E−05 0.00 0.71 283 chr10-28832 10 102498899 102499830 −19.04 3.04 1.35E−07 3.19E−05 0.79 0.21 284 chr6-3486 6 26853218 26853781 −19.52 2.96 1.37E−07 3.22E−05 0.00 0.00 285 chr13-12246 13 111759028 111761130 −16.01 2.34 1.37E−07 3.22E−05 0.78 0.22 286 chr4-8602 4 177223716 177224452 −22.13 5.57 1.37E−07 3.22E−05 0.60 0.40 287 chr3-19000 3 131176181 131177707 −16.42 2.39 1.41E−07 3.29E−05 0.88 0.12 288 chr20-22989 20 61108502 61108900 −34.83 30.37 1.42E−07 3.31E−05 1.00 0.00 289 chr1-47207 1 75368128 75368976 −18.44 2.52 1.54E−07 3.56E−05 0.41 0.59 290 chr20-1414 20 20292251 20294265 −17.48 2.48 1.58E−07 3.65E−05 0.93 0.07 291 chr7-14221 7 87067400 87068467 −19.47 3.14 1.60E−07 3.69E−05 0.51 0.49 292 chr6-15406 6 27913755 27915100 −20.72 4.18 1.68E−07 3.85E−05 0.75 0.25 293 chr6-20418 6 28885462 28886854 −19.13 2.89 1.69E−07 3.87E−05 0.00 0.00 294 chr19-30142 19 57564768 57565451 −20.69 3.82 1.71E−07 3.92E−05 0.71 0.29 295 chr19-48169 19 62309903 62310319 −18.86 2.73 1.75E−07 3.98E−05 0.86 0.14 296 chr12-16245 12 131991417 131991716 −19.63 3.11 1.76E−07 4.00E−05 0.00 0.00 297 chr2-25081 2 171386257 171388430 −18.47 2.67 1.83E−07 4.14E−05 0.75 0.25 298 chr16-43212 16 85088677 85089077 −20.08 3.42 1.85E−07 4.18E−05 1.00 0.00 299 chr8-9764 8 57394577 57395494 −18.26 2.57 1.87E−07 4.21E−05 0.46 0.54 300 chr5-10265 5 145705002 145706101 −17.62 2.42 1.91E−07 4.29E−05 0.51 0.49 301 chr5-14304 5 452636 453644 −17.49 2.43 1.91E−07 4.29E−05 0.00 0.00 302 chr19-41383 19 35408947 35409765 −19.86 3.23 1.93E−07 4.33E−05 1.00 0.00 303 chr6-18714 6 26854543 26855628 −20.08 3.67 1.93E−07 4.33E−05 0.00 0.00 304 chr10-18957 10 133000272 133001373 −17.01 2.38 1.99E−07 4.43E−05 0.70 0.30 305 chr11-25281 11 124240101 124242276 −18.44 2.50 2.00E−07 4.44E−05 0.64 0.36 306 chr6-6380 6 28475091 28475975 −19.12 2.78 2.07E−07 4.58E−05 0.60 0.40 307 chr10-11832 10 23523652 23524551 −16.63 2.32 2.12E−07 4.68E−05 0.67 0.33 308 chr1-40817 1 178468989 178469844 −17.42 2.47 2.15E−07 4.74E−05 1.00 0.00 309 chr12-30179 12 109611561 109612145 −21.14 4.29 2.18E−07 4.80E−05 0.29 0.71 310 chr16-38440 16 66120313 66122195 −19.92 3.05 2.21E−07 4.86E−05 0.45 0.55 311 chr18-7308 18 74837592 74839685 −16.79 2.37 2.22E−07 4.86E−05 0.81 0.19 312 chr7-34695 7 6536641 6537912 −20.75 4.15 2.25E−07 4.91E−05 0.50 0.50 313 chr17-32969 17 47591129 47592954 −18.83 2.74 2.30E−07 5.02E−05 0.18 0.82 314 chr3-9862 3 148609673 148611987 −15.54 2.26 2.42E−07 5.23E−05 0.87 0.13 315 chr18-4595 18 19071802 19072183 −21.06 3.80 2.50E−07 5.40E−05 0.00 0.00 316 chr19-20838 19 20974468 20975124 −20.74 3.76 2.55E−07 5.48E−05 0.00 0.00 317 chr20-21706 20 238489 239288 −18.87 2.61 2.89E−07 6.16E−05 0.00 0.00 318 chr19-22015 19 20398075 20398528 −19.56 3.17 2.92E−07 6.19E−05 0.00 0.00 319 chr12-4418 12 101882694 101883147 −21.25 4.18 3.04E−07 6.40E−05 0.00 1.00 320 chr19-31732 19 62702907 62703289 −22.35 5.19 3.05E−07 6.40E−05 0.92 0.08 321 chr9-18934 9 132805164 132805693 −22.41 5.08 3.05E−07 6.40E−05 0.83 0.17 322 chr3-3854 3 188870124 188871016 −18.09 2.45 3.14E−07 6.59E−05 0.29 0.71 323 chr13-8627 13 69579522 69580526 −18.59 2.57 3.15E−07 6.61E−05 0.48 0.52 324 chr1-11343 1 177978096 177979141 −18.99 2.59 3.20E−07 6.69E−05 0.55 0.45 325 chr13-16458 13 111764441 111766059 −17.26 2.34 3.27E−07 6.81E−05 0.18 0.82 326 chr9-13838 9 99649709 99650308 −21.40 3.94 3.28E−07 6.82E−05 0.00 1.00 327 chr14-17061 14 100613465 100614257 −19.40 3.10 3.29E−07 6.83E−05 0.00 0.00 328 chr7-12415 7 72675186 72676378 −19.84 3.40 3.42E−07 7.05E−05 0.77 0.23 329 chr6-7903 6 336105 336557 −19.30 3.01 3.53E−07 7.23E−05 0.81 0.19 330 chr15-5377 15 32833697 32834284 −19.87 2.97 3.60E−07 7.35E−05 0.93 0.07 331 chr19-33708 19 55852950 55854597 −18.10 2.42 3.62E−07 7.37E−05 0.36 0.64 332 chr8-1848 8 55544961 55545861 −16.92 2.30 3.62E−07 7.37E−05 0.61 0.39 333 chr20-22464 20 21436219 21437286 −18.21 2.41 3.63E−07 7.39E−05 1.00 0.00 334 chr14-13812 14 56347638 58348186 −18.82 2.52 3.68E−07 7.47E−05 0.40 0.60 335 chr7-18218 7 117299526 117300618 −19.47 2.84 3.75E−07 7.56E−05 0.40 0.60 336 chr18-10939 18 53246045 53247374 −16.25 2.27 3.84E−07 7.70E−05 0.95 0.05 337 chr1-49613 1 85300309 85300783 −34.93 30.17 3.95E−07 7.88E−05 0.00 0.00 338 chr8-4423 3 114513510 114514431 −17.52 2.26 3.94E−07 7.88E−05 0.28 0.72 339 chr9-21477 9 123538279 123538578 −20.48 3.56 3.95E−07 7.88E−05 0.81 0.19 340 chr7-10468 7 71438612 71439608 −18.01 2.49 4.03E−07 8.02E−05 0.92 0.08 341 chr7-36589 7 20789757 20791421 −16.82 2.32 4.06E−07 8.08E−05 0.95 0.05 342 chr3-8813 3 37008980 37010487 −21.65 4.52 4.11E−07 8.15E−05 0.75 0.25 343 chr5-8897 5 1938336 1940120 −17.69 2.49 4.14E−07 8.20E−05 1.00 0.00 344 chr8-19800 8 65443925 65445545 −16.28 2.25 4.21E−07 8.33E−05 0.67 0.33 345 chr10-3695 10 106389441 106390669 −18.82 2.69 4.34E−07 8.56E−05 0.90 0.10 346 chr7-36476 7 19149605 19150556 −18.55 2.36 4.40E−07 8.67E−05 0.00 1.00 347 chr15-2485 15 58083248 58086233 −16.29 2.25 4.43E−07 8.72E−05 0.80 0.20 348 chr13-3613 13 78067586 78069656 −16.23 2.21 4.62E−07 9.00E−05 0.54 0.46 349 chr10-6953 10 22673906 22675235 −15.71 2.19 4.73E−07 9.15E−05 0.65 0.35 350 chr7-15475 7 97198854 97199710 −17.08 2.27 4.90E−07 9.41E−05 0.75 0.25 351 chr7-38573 7 154944311 154945854 −17.99 2.39 5.01E−07 9.57E−05 0.26 0.74 352 chr6-17667 6 28851357 28851980 −19.57 2.68 5.33E−07 1.01E−04 0.00 0.00 353 chr4-7295 4 21558920 21559523 −19.46 2.88 5.42E−07 1.02E−04 0.43 0.57 354 chr1-13372 1 147422212 147423512 −17.77 2.35 5.47E−07 1.03E−04 0.43 0.57 355 chr8-30753 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59138496 −18.48 2.47 1.03E−06 1.74E−04 0.35 0.65 395 chr5-23290 5 6740085 6740486 −22.28 5.29 1.03E−06 1.74E−04 0.00 0.00 396 chr5-7502 5 15553610 15554084 −21.75 4.39 1.03E−06 1.74E−04 0.66 0.34 397 chr18-5039 18 55082452 55084373 −17.58 2.33 1.03E−06 1.75E−04 0.32 0.68 398 chr11-11623 11 32404535 32407465 −16.44 2.15 1.04E−06 1.77E−04 0.51 0.49 399 chr6-9424 6 50797741 50798040 −20.15 2.79 1.08E−06 1.82E−04 0.00 0.00 400 chr5-13092 5 1940356 1941066 −20.18 3.10 1.11E−06 1.85E−04 0.55 0.45 401 chr12-24670 12 4249005 4250035 −19.14 2.45 1.12E−06 1.87E−04 1.00 0.00 402 chr1-53988 1 75369036 75371706 −17.40 2.18 1.14E−06 1.91E−04 0.08 0.92 403 chr19-44228 19 3385736 3386568 −17.43 2.23 1.16E−06 1.94E−04 0.59 0.41 404 chr9-9221 9 94609232 94612099 −17.54 2.25 1.23E−06 2.03E−04 0.99 0.01 405 chr3-12106 3 171785722 171786096 −17.86 2.28 1.24E−06 2.06E−04 0.96 0.04 406 chr3-22823 3 61522661 61524867 −18.62 2.32 1.30E−06 2.14E−04 0.93 0.07 407 chr11-20709 11 716315 716939 −18.48 2.45 1.30E−06 2.15E−04 0.89 0.11 408 chr8-13120 8 77756536 77757627 −17.64 2.30 1.33E−06 2.18E−04 0.55 0.45 409 chr19-5399 19 45006599 45007416 −20.87 3.49 1.34E−06 2.20E−04 0.27 0.73 410 chr4-14651 4 147780313 147781689 −16.48 2.12 1.37E−06 2.23E−04 0.75 0.25 411 chr1-55770 1 57660234 57662326 −19.18 2.81 1.43E−06 2.33E−04 0.85 0.15 412 chr13-10642 13 111771075 111771643 −17.27 2.21 1.43E−06 2.34E−04 0.89 0.11 413 chr19-13959 19 61571120 61571844 −20.04 3.02 1.45E−06 2.35E−04 0.80 0.20 414 chr9-31375 9 29202550 29202931 −21.22 4.15 1.48E−06 2.39E−04 0.71 0.29 415 chr7-16609 7 154942132 154942742 −20.61 3.37 1.54E−06 2.48E−04 1.00 0.00 416 chr6-21066 6 146391658 146392926 −18.05 2.19 1.54E−06 2.49E−04 0.20 0.80 417 chr18-3786 18 43040367 43044918 −15.82 2.07 1.55E−06 2.49E−04 0.30 0.70 418 chr9-24850 9 22436668 22437704 −22.10 5.56 1.55E−06 2.50E−04 0.98 0.02 419 chr12-2820 12 30839655 30841565 −17.79 2.35 1.57E−06 2.53E−04 0.63 0.37 420 chr7-12265 7 121743462 121744765 −16.43 2.11 1.59E−06 2.56E−04 0.61 0.39 421 chr9-16243 9 78826045 78826344 −20.90 3.42 1.63E−06 2.60E−04 0.00 1.00 422 chr22-24548 22 47264373 47264866 −22.24 5.28 1.65E−06 2.64E−04 1.00 0.00 423 chr7-6402 7 8447245 8450849 −15.53 2.06 1.66E−06 2.65E−04 0.38 0.62 424 chr10-18190 10 57057212 57058581 −18.16 2.17 1.67E−06 2.66E−04 0.34 0.66 425 chr17-16808 17 58969065 58989919 −20.06 2.81 1.68E−06 2.66E−04 0.75 0.25 426 chr12-16783 12 113369423 113370090 −19.47 2.67 1.69E−06 2.68E−04 0.47 0.53 427 chr6-21444 6 28522662 28523159 −18.71 2.41 1.70E−06 2.69E−04 0.00 0.00 428 chr18-1098 18 5186191 5187783 −17.51 2.17 1.77E−06 2.79E−04 0.72 0.28 429 chr19-11665 19 62786387 62787669 −17.69 2.22 1.79E−06 2.81E−04 0.80 0.20 430 chr4-25870 4 46689372 46691121 −17.85 2.21 1.84E−06 2.88E−04 0.43 0.57 431 chr13-11476 13 111763091 111764344 −18.00 2.23 1.97E−06 3.04E−04 0.70 0.30 432 chr16-21001 16 67101292 67102016 −18.85 2.37 1.97E−06 3.05E−04 0.52 0.48 433 chr9-11097 9 78818499 78820515 −17.42 2.23 1.98E−06 3.06E−04 0.35 0.65 434 chr6-15498 6 50799906 50800777 −18.48 2.39 2.01E−06 3.09E−04 0.26 0.74 435 chr8-14169 8 56596097 56596396 −35.00 30.04 2.03E−06 3.12E−04 0.00 0.00 436 chr12-10547 12 73889129 73889832 −18.50 2.26 2.03E−06 3.13E−04 0.50 0.50 437 chr3-13593 3 182923564 182924686 −17.77 2.18 2.04E−06 3.13E−04 0.00 0.00 438 chr3-22674 3 171786097 171786716 −18.67 2.44 2.06E−06 3.16E−04 0.59 0.41 439 chr4-6479 4 6251718 6252521 −21.16 4.11 2.09E−06 3.19E−04 0.97 0.03 440 chr8-6273 8 56177470 56178405 −17.12 2.19 2.09E−06 3.19E−04 0.90 0.10 441 chr17-1165 17 5915052 5915590 −22.29 5.24 2.11E−06 3.21E−04 0.23 0.77 442 chr19-16291 19 63137956 63138704 −19.93 3.10 2.12E−06 3.23E−04 0.62 0.38 443 chr10-18652 10 102311731 102312878 −17.20 2.16 2.19E−06 3.32E−04 0.67 0.33 444 chr8-8079 3 50984817 50986296 −17.23 2.11 2.21E−06 3.35E−04 0.40 0.60 445 chr10-28230 10 50273292 50274913 −18.01 2.29 2.22E−06 3.36E−04 1.00 0.00 446 chr18-5240 18 43030844 43032407 −17.75 2.16 2.28E−06 3.43E−04 0.29 0.71 447 chr8-27248 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2.90 4.32E−06 5.88E−04 0.76 0.24 487 chr7-9575 7 35267289 35288488 −17.05 2.05 4.38E−06 5.94E−04 0.48 0.52 488 chr6-28662 6 117691091 117691644 −18.21 2.21 4.41E−06 5.97E−04 0.42 0.58 489 chr2-33315 2 98330298 98331540 −18.09 2.10 4.53E−06 6.10E−04 0.26 0.74 490 chr1-19681 1 154860239 154861211 −20.55 3.39 4.55E−06 6.12E−04 0.96 0.04 491 chr6-7421 6 100167235 100168199 −17.46 2.10 4.60E−06 6.17E−04 0.52 0.48 492 chr10-23988 10 23503516 23504392 −18.13 2.07 4.64E−06 6.20E−04 0.43 0.57 493 chr1-33096 1 75374659 75375886 −17.46 2.14 4.67E−06 6.24E−04 0.66 0.34 494 chr13-14599 13 111756947 111759018 −16.76 2.04 4.69E−06 6.25E−04 0.55 0.45 495 chr1-29436 1 50662305 50664209 −17.08 2.04 4.85E−06 6.45E−04 0.92 0.08 496 chr13-5185 13 111755582 111758946 −17.12 2.04 4.87E−06 6.46E−04 0.65 0.35 497 chr10-19113 10 118889398 118890307 −17.17 2.07 4.87E−06 6.47E−04 1.00 0.00 498 chr8-18053 3 110772419 110773490 −18.40 2.27 4.89E−06 6.48E−04 0.46 0.54 499 chr16-10222 16 81217864 81218691 −20.60 3.30 4.92E−06 6.52E−04 0.65 0.35 500 chr8-8662 8 65449063 65449743 −18.49 2.13 5.03E−06 6.65E−04 0.22 0.78 501 chr9-29496 9 137746214 137746513 −21.49 3.67 5.14E−06 6.77E−04 0.96 0.04 502 chr10-21536 10 111206790 111207168 −18.82 2.34 5.18E−06 6.82E−04 0.75 0.25 503 chr3-9248 3 79899047 79900065 −20.04 2.74 5.18E−06 6.82E−04 0.00 1.00 504 chr18-14616 18 68686390 68687284 −17.67 2.16 5.25E−06 6.89E−04 1.00 0.00 505 chr18-4761 18 68684752 68685778 −17.69 2.17 5.26E−06 6.91E−04 0.81 0.19 506 chr14-7997 14 60021645 60022784 −17.05 2.01 5.28E−06 6.93E−04 0.85 0.15 507 chr1-52773 1 165156276 165157660 −16.45 1.99 5.31E−06 6.96E−04 0.63 0.37 508 chr3-19953 3 169449828 169451332 −20.39 3.04 5.33E−06 6.96E−04 0.59 0.41 509 chr6-9636 6 12857279 12857578 −20.42 2.98 5.33E−06 6.96E−04 0.00 1.00 510 chr1-48028 1 179719201 179719802 −20.01 2.83 5.40E−06 7.06E−04 0.61 0.39 511 chr16-43017 16 6009572 6010985 −17.79 2.08 5.45E−06 7.11E−04 0.59 0.41 512 chr6-10546 6 74081214 74081830 −17.04 2.04 5.59E−06 7.26E−04 0.86 0.14 513 chr11-25392 11 20185641 20186213 −19.46 2.50 5.64E−06 7.31E−04 0.00 0.00 514 chr1-11916 1 147194313 147195312 −16.49 1.98 5.67E−06 7.34E−04 0.00 0.00 515 chr8-12141 8 67507023 67507666 −18.55 2.42 5.83E−06 7.51E−04 0.76 0.24 516 chr5-12906 5 145698009 145700619 −16.65 2.00 6.03E−06 7.71E−04 0.69 0.31 517 chr6-16814 6 58255054 58255979 −18.59 2.25 6.09E−06 7.78E−04 0.00 0.00 518 chr12-22778 12 106692971 106693781 −18.60 2.29 6.29E−06 8.00E−04 0.72 0.28 519 chr20-4865 20 21449236 21452072 −17.37 2.07 6.31E−06 8.01E−04 0.69 0.31 520 chr11-24352 11 113434777 113435377 −22.31 5.14 6.46E−06 8.18E−04 0.89 0.11 521 chr17-35096 17 43127241 43128186 −19.21 2.32 6.59E−06 8.33E−04 0.79 0.21 522 chr14-2805 14 36061285 36062365 −17.10 2.00 6.94E−06 8.72E−04 0.83 0.17 523 chr3-18149 3 148569747 148570426 −18.95 2.21 7.10E−06 8.90E−04 0.00 0.00 524 chr6-12241 6 28850875 28851174 −21.13 3.14 7.20E−06 8.99E−04 0.00 0.00 525 chr9-11022 9 32772588 32773890 −17.61 2.10 7.25E−06 9.05E−04 0.53 0.47 526 chr11-31615 11 131285836 131287061 −17.66 2.06 7.28E−06 9.08E−04 0.74 0.26 527 chr20-17172 20 34603307 34803606 −20.48 2.87 7.31E−06 9.11E−04 0.00 1.00 528 chr11-1210 11 32416010 32417947 −17.12 2.03 7.35E−06 9.14E−04 0.45 0.55 529 chr13-15917 13 27395919 27396699 −18.25 2.12 7.39E−06 9.18E−04 0.61 0.39 530 chr5-12543 5 37876045 37876600 −21.03 3.68 7.43E−06 9.22E−04 0.79 0.21 531 chr12-24630 12 45760774 45761158 −35.14 29.75 7.59E−06 9.39E−04 0.00 1.00 532 chr18-12033 18 5618866 5620739 −18.49 2.38 7.69E−06 9.48E−04 1.00 0.00 533 chr1-35819 1 34401107 34403040 −19.90 2.91 7.69E−06 9.48E−04 0.86 0.14 534 chr3-13473 3 140140990 140141890 −17.58 2.07 7.85E−06 9.67E−04 0.89 0.11 535 chr14-4809 14 41148725 41149233 −20.05 2.65 7.87E−06 9.67E−04 0.00 0.00 536 chr8-6564 8 114514742 114515804 −17.44 1.96 7.87E−06 9.67E−04 0.00 1.00 537 chr4-2028 4 36921605 36923906 −17.92 2.12 7.92E−06 9.72E−04 0.75 0.25 538 chr6-13773 6 166341901 166342916 −18.37 2.26 7.96E−06 9.76E−04 0.33 0.67 539 chr9-17317 9 99650551 99651573 −17.19 1.98 8.06E−06 9.87E−04 0.77 0.23 540 chr19-26183 19 17658705 17660538 −19.92 2.74 8.10E−06 9.91E−04 0.38 0.62 541 chr9-32778 9 832501 833578 −16.77 1.97 8.33E−06 1.01E−03 0.80 0.20 542 chr1-20354 1 90954471 90955463 −17.67 2.05 8.75E−06 1.06E−03 0.62 0.38 543 chr19-36386 19 22781128 22782049 −18.26 2.10 8.76E−06 1.06E−03 0.22 0.78 544 chr9-9261 9 16716795 16717470 −22.56 4.76 8.84E−06 1.06E−03 0.61 0.39 545 chr1-28592 1 210939995 210941410 −21.03 3.62 8.97E−06 1.08E−03 0.48 0.52 546 chr6-27327 6 28282873 28284334 −17.06 1.95 9.09E−06 1.09E−03 0.22 0.78 547 chr13-5914 13 27268702 27269918 −20.00 2.87 9.19E−06 1.10E−03 0.21 0.79 548 chr19-36571 19 24061637 24062272 −20.74 3.48 9.30E−06 1.11E−03 0.00 0.00 549 chr17-8384 17 32377397 32377860 −18.94 2.21 9.32E−06 1.11E−03 0.62 0.38 550 chr18-12335 18 43027885 43028280 −18.54 2.25 9.41E−06 1.12E−03 1.00 0.00 551 chr1-19649 1 20682759 20684507 −19.80 2.69 9.74E−06 1.15E−03 0.83 0.17 552 chr1-6836 1 38002381 38003734 −17.94 2.05 9.76E−06 1.16E−03 0.78 0.22 553 chr2-4079 2 176639782 176641943 −16.41 1.92 9.85E−06 1.17E−03 0.65 0.35 554 chr2-42491 2 118783832 118784739 −20.85 3.32 1.05E−05 1.24E−03 0.00 0.00 555 chr6-6376 6 101956346 101957787 −16.51 1.91 1.12E−05 1.30E−03 0.81 0.19 556 chr13-11038 13 83350909 83352653 −16.71 1.92 1.13E−05 1.31E−03 0.13 0.87 557 chr8-2655 8 77747628 77748201 −18.52 2.03 1.13E−05 1.31E−03 0.00 0.00 558 chr20-12429 20 22510293 22512770 −16.53 1.93 1.17E−05 1.35E−03 0.82 0.18 559 chr16-33967 16 49704874 49705374 −19.16 2.31 1.19E−05 1.37E−03 0.76 0.24 560 chr3-23796 3 61524948 61525699 −18.00 2.07 1.21E−05 1.38E−03 0.31 0.69 561 chr12-23224 12 127317974 127319200 −17.94 1.98 1.22E−05 1.39E−03 0.92 0.08 562 chr20-4778 20 54013046 54014804 −16.63 1.90 1.22E−05 1.39E−03 0.59 0.41 563 chr11-27538 11 8146412 8147571 −18.72 2.24 1.28E−05 1.44E−03 0.38 0.62 564 chr13-5196 13 52321726 52322229 −20.41 2.76 1.29E−05 1.45E−03 0.00 1.00 565 chr9-19031 9 23821077 23821585 −20.55 3.25 1.30E−05 1.46E−03 0.00 0.00 566 chr14-16431 14 56340466 56341025 −19.44 2.13 1.33E−05 1.49E−03 0.00 0.00 567 chr16-31470 16 86192406 86193085 −21.37 3.88 1.33E−05 1.49E−03 0.00 1.00 568 chr3-7330 3 44728954 44729579 −20.72 3.18 1.37E−05 1.53E−03 0.48 0.52 569 chr12-13086 12 112513962 112514340 −35.19 29.64 1.40E−05 1.56E−03 0.46 0.54 570 chr2-11853 2 127498721 127500096 −18.21 2.07 1.40E−05 1.56E−03 0.32 0.68 571 chr9-28476 9 16861414 16862101 −22.64 4.60 1.40E−05 1.56E−03 0.88 0.12 572 chr8-9961 8 55528201 55529158 −17.37 1.94 1.42E−05 1.57E−03 0.44 0.56 573 chr1-26841 1 219131024 219132359 −16.30 1.87 1.44E−05 1.59E−03 0.53 0.47 574 chr12-26855 12 103221159 103222245 −17.32 1.94 1.46E−05 1.61E−03 0.59 0.41 575 chr8-13399 8 23618273 23621219 −15.89 1.85 1.57E−05 1.72E−03 0.92 0.08 576 chr7-33041 7 136203783 138208921 −17.18 1.95 1.59E−05 1.74E−03 0.75 0.25 577 chr7-3549 7 93041451 93043195 −19.64 2.71 1.60E−05 1.75E−03 0.17 0.83 578 chr11-5813 11 17696719 17699162 −16.86 1.92 1.60E−05 1.75E−03 0.74 0.26 579 chr3-20990 3 113534558 113534857 −21.79 4.21 1.62E−05 1.76E−03 0.92 0.08 580 chr5-15770 5 170677426 170678175 −21.79 4.15 1.62E−05 1.76E−03 0.00 1.00 581 chr9-33024 9 70978335 70978728 −35.20 29.63 1.64E−05 1.78E−03 0.49 0.51 582 chr20-11499 20 61055725 61056352 −19.10 2.33 1.70E−05 1.84E−03 0.00 1.00 583 chr9-31522 9 36729494 36730076 −17.91 1.98 1.78E−05 1.91E−03 0.43 0.57 584 chr12-29205 12 127316828 127317529 −18.39 2.06 1.78E−05 1.92E−03 0.76 0.24 585 chr10-20181 10 70998508 70998807 −21.40 3.77 1.83E−05 1.96E−03 0.00 0.00 586 chr8-13946 8 116729607 116729987 −17.79 1.96 1.84E−05 1.96E−03 0.83 0.17 587 chr10-22036 10 106388504 106389138 −20.63 2.89 1.96E−05 2.08E−03 0.00 1.00 588 chr19-41265 19 1082598 1083086 −20.62 2.74 1.96E−05 2.08E−03 0.00 0.00 589 chr1-23365 1 226723970 226724548 −17.91 1.98 1.98E−05 2.10E−03 0.37 0.63 590 chr9-2514 9 23814089 23815144 −18.12 2.02 1.99E−05 2.11E−03 0.00 0.55 591 chr18-12277 18 53171722 53172797 −17.33 1.92 2.03E−05 2.15E−03 0.82 0.18 592 chr1-40928 1 167662928 167663578 −18.38 1.95 2.07E−05 2.18E−03 0.38 0.62 593 chr5-22310 5 140767601 140767900 −19.05 2.21 2.11E−05 2.21E−03 0.90 0.10 594 chr1-52163 1 147168367 147168845 −17.24 1.92 2.17E−05 2.27E−03 1.00 0.00 595 chr3-13919 3 62330759 62332438 −16.49 1.83 2.17E−05 2.27E−03 0.48 0.52 596 chr16-40286 16 49725701 49726347 −18.38 2.07 2.17E−05 2.27E−03 0.90 0.10 597 chr11-5662 11 46367350 46369651 −20.42 2.83 2.23E−05 2.32E−03 0.94 0.06 598 chr19-31963 19 23445448 23446281 −18.12 2.02 2.24E−05 2.33E−03 0.66 0.34 599 chr17-40131 17 76929312 76930082 −17.39 1.92 2.28E−05 2.36E−03 0.68 0.32 600 chr6-1289 8 28848985 28849457 −20.47 2.78 2.30E−05 2.38E−03 0.00 0.00 601 chr3-22112 3 148624151 148625148 −18.01 1.93 2.37E−05 2.44E−03 0.21 0.79 602 chr18-4284 18 53265397 53265773 −22.45 4.91 2.41E−05 2.47E−03 0.00 0.00 603 chr11-23138 11 31780750 31781049 −19.74 2.36 2.45E−05 2.51E−03 0.00 1.00 604 chr17-38402 17 17567720 17569033 −19.53 2.40 2.46E−05 2.52E−03 1.00 0.00 605 chr8-20019 8 121206240 121207734 −16.97 1.88 2.50E−05 2.56E−03 0.55 0.45 606 chr19-14446 19 22260848 22261568 −17.99 1.93 2.52E−05 2.57E−03 0.44 0.56 607 chr19-26167 19 61517339 61518102 −35.21 29.62 2.64E−05 2.67E−03 0.68 0.32 608 chr6-1806 6 58256334 58257651 −17.62 1.87 2.75E−05 2.77E−03 0.00 0.00 609 chr10-22333 10 106430554 106431167 −21.24 3.36 2.81E−05 2.82E−03 0.00 0.00 610 chr20-17950 20 62181948 62182625 −20.60 2.94 2.96E−05 2.95E−03 0.36 0.64 611 chr9-22655 9 70978942 70979510 −20.80 3.30 2.96E−05 2.95E−03 0.74 0.26 612 chr10-26762 10 118880706 118881322 −17.83 1.94 3.03E−05 3.01E−03 0.00 1.00 613 chr11-34612 11 31805046 31805901 −18.01 2.00 3.03E−05 3.01E−03 0.34 0.66 614 chr14-10016 14 37747939 37750251 −15.87 1.77 3.05E−05 3.03E−03 0.97 0.03 615 chr19-30352 19 9469636 9470723 −17.89 1.92 3.10E−05 3.07E−03 0.50 0.50 616 chr8-21157 8 93176592 93176969 −20.08 2.61 3.14E−05 3.10E−03 0.00 0.00 617 chr6-25890 8 10990659 10991273 −17.74 1.95 3.14E−05 3.10E−03 0.36 0.64 618 chr4-18262 4 5942748 5943766 −18.33 1.90 3.16E−05 3.12E−03 0.58 0.42 619 chr7-37861 7 157173808 157176308 −16.08 1.77 3.19E−05 3.14E−03 1.00 0.00 620 chr2-45508 2 232959226 232950738 −15.58 1.76 3.26E−05 3.20E−03 0.75 0.25 621 chr8-20522 8 145131929 145132492 −21.56 3.56 3.31E−05 3.24E−03 0.00 0.00 622 chr16-23007 16 86179871 86180426 −20.60 2.72 3.37E−05 3.30E−03 0.00 0.00 623 chr12-10548 12 70952716 70954501 −18.07 1.99 3.38E−05 3.30E−03 0.62 0.38 624 chr6-21369 5 12857751 12858050 −21.47 3.60 3.49E−05 3.40E−03 0.62 0.38 625 chr19-17643 19 10489290 10490623 −20.44 2.80 3.58E−05 3.48E−03 0.10 0.90 626 chr7-21508 7 104372640 104373101 −22.69 4.50 3.65E−05 3.53E−03 0.00 0.00 627 chr1-19467 1 145010595 145011162 −17.35 1.79 3.67E−05 3.55E−03 0.80 0.20 628 chr11-5760 11 29994166 29994552 −19.17 2.08 3.87E−05 3.72E−03 0.70 0.30 629 chr3-6248 3 14826633 14827091 −20.27 2.51 3.88E−05 3.72E−03 0.78 0.22 630 chr16-39747 16 12901915 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−19.92 2.45 4.89E−05 4.51E−03 0.00 0.00 644 chr11-10407 11 122355001 122355564 −18.13 1.93 4.93E−05 4.55E−03 0.00 1.00 645 chr9-12742 9 97264393 97265064 −19.83 2.11 5.02E−05 4.62E−03 0.00 0.00 646 chr16-6800 16 47870657 47872951 −17.83 1.88 5.06E−05 4.64E−03 0.62 0.38 647 chr19-28830 19 22977311 22978162 −17.33 1.81 5.20E−05 4.75E−03 0.57 0.43 648 chr3-18921 3 161239255 161239880 −20.78 2.69 5.21E−05 4.76E−03 0.58 0.42 649 chr7-20736 7 154933652 154937288 −16.16 1.72 5.21E−05 4.76E−03 0.75 0.25 650 chr11-5754 11 31782242 31783996 −16.93 1.78 5.26E−05 4.79E−03 0.70 0.30 651 chr11-26384 11 124243246 124244782 −17.75 1.80 5.29E−05 4.81E−03 0.19 0.81 652 chr6-10449 6 27343730 27344104 −18.05 1.86 5.33E−05 4.84E−03 0.00 0.00 653 chr17-34300 17 70178657 70179522 −17.37 1.81 5.36E−05 4.87E−03 0.59 0.41 654 chr7-31210 7 154995248 154996216 −17.09 1.76 5.38E−05 4.88E−03 0.69 0.31 655 chr3-1810 3 132563160 132564028 −16.80 1.76 5.81E−05 5.22E−03 0.71 0.29 656 chr12-2151 12 128953476 128954156 −18.07 1.88 5.86E−05 5.26E−03 0.87 0.13 657 chr10-2387 10 6282716 6283428 −35.25 29.52 5.99E−05 5.35E−03 0.63 0.37 658 chr11-33859 11 117171063 117172707 −18.39 1.92 6.02E−05 5.37E−03 0.52 0.48 659 chr2-24491 2 219905312 219905929 −19.21 2.10 6.27E−05 5.56E−03 0.00 1.00 660 chr3-9414 3 148557064 148557363 −21.78 3.25 6.37E−05 5.63E−03 0.00 0.00 661 chr6-1661 6 63053409 63054691 −17.48 1.81 6.51E−05 5.74E−03 0.29 0.71 662 chr11-13605 11 20137690 20138947 −17.28 1.77 6.77E−05 5.94E−03 0.89 0.11 663 chr13-9261 13 48692492 48693637 −16.34 1.71 6.78E−05 5.94E−03 0.92 0.08 664 chr1-55159 1 243396381 243396680 −20.63 2.85 7.01E−05 6.12E−03 0.00 0.00 665 chr13-8903 13 95092320 95092694 −18.35 1.96 7.09E−05 6.16E−03 0.76 0.24 666 chr19-35894 19 62302287 62302885 −18.71 1.99 7.10E−05 6.17E−03 0.42 0.58 667 chr18-11477 18 53248374 53249217 −20.74 2.73 7.11E−05 6.18E−03 0.00 1.00 668 chr19-6899 19 58503272 58503954 −18.92 1.90 7.50E−05 6.46E−03 0.57 0.43 669 chr6-16690 6 28749581 28750455 −19.98 2.49 7.70E−05 6.61E−03 0.00 0.00 670 chr3-10180 3 174597694 174598312 −22.41 4.93 7.86E−05 6.73E−03 0.35 0.65 671 chr5-10230 5 63290659 63291554 −19.64 2.32 7.90E−05 6.76E−03 0.41 0.59 672 chr19-19483 19 60850975 60851445 −20.33 2.80 7.94E−05 6.78E−03 0.80 0.20 673 chr12-7564 12 46863133 46865076 −17.48 1.75 8.50E−05 7.20E−03 0.62 0.38 674 chr11-24671 11 133332035 133333054 −19.10 1.88 8.64E−05 7.31E−03 0.62 0.38 675 chr19-19145 19 63301076 63301887 −21.63 3.40 8.92E−05 7.51E−03 0.80 0.20 676 chr7-9141 7 96470160 96471563 −17.32 1.72 9.24E−05 7.74E−03 0.00 1.00 677 chr3-23572 3 148596152 148597503 −15.98 1.65 9.24E−05 7.75E−03 0.64 0.36 678 chr11-25406 11 31775677 31779112 −16.36 1.67 9.26E−05 7.75E−03 0.39 0.61 679 chr17-5347 17 55573396 55573950 −19.19 2.12 9.46E−05 7.90E−03 0.54 0.46 680 chr11-22903 11 62315518 62316198 −21.30 3.18 9.56E−05 7.97E−03 0.67 0.33 681 chr6-16740 6 28518900 28519531 −18.79 1.83 9.67E−05 8.05E−03 0.00 0.00 682 chr6-12588 6 12400479 12400778 −18.87 1.92 9.77E−05 8.13E−03 0.00 0.00 683 chr1-24925 1 147490921 147491510 −19.11 1.90 9.90E−05 8.22E−03 0.48 0.52 684 chr1-42512 1 199729768 199730067 −19.61 2.20 9.93E−05 8.23E−03 0.00 0.00 685 chr19-47868 19 3161711 3162010 −35.33 29.38 9.95E−05 8.24E−03 0.00 0.00 686 chr19-31544 19 62911410 62912734 −16.73 1.71 1.01E−04 8.33E−03 0.32 0.68 687 chr11-15137 11 45333436 45333891 −22.44 4.86 1.01E−04 8.36E−03 0.68 0.32 688 chr18-5740 18 73740881 73741255 −21.13 3.34 1.04E−04 8.57E−03 0.60 0.40 689 chr20-19704 20 61840600 61841345 −20.06 2.43 1.07E−04 8.77E−03 1.00 0.00 690 chr7-10983 7 35460718 35461475 −16.75 1.66 1.11E−04 9.01E−03 0.65 0.35 691 chr13-11675 13 94453413 94453712 −20.28 2.36 1.14E−04 9.25E−03 0.35 0.65 692 chr8-7098 8 143702377 143702833 −19.52 2.21 1.15E−04 9.30E−03 0.00 0.00 693 chr3-24896 3 148590966 148592290 −16.05 1.63 1.15E−04 9.30E−03 0.82 0.18 694 chr11-18108 11 31802820 31804364 −17.07 1.70 1.17E−04 9.44E−03 0.42 0.58 695 chr2-31496 2 241408904 241409558 −22.80 4.29 1.18E−04 9.51E−03 0.84 0.16 696 chr17-25891 17 39338985 39340161 −21.49 3.53 1.19E−04 9.55E−03 0.41 0.59 697 chr3-13418 3 148573160 148573538 −21.09 2.96 1.19E−04 9.55E−03 0.00 0.00 698 chr16-15120 16 84877272 84878635 −18.80 1.92 1.19E−04 9.57E−03 0.84 0.16 699 chr13-9410 13 107317793 107319138 −18.71 2.02 1.22E−04 9.76E−03 0.95 0.05 700 chr11-23818 11 19691771 19693088 −20.31 2.46 1.23E−04 9.84E−03 0.10 0.90 701 chr6-12015 6 27628983 27629386 −20.25 2.52 1.23E−04 9.85E−03 0.00 0.00 702 chr19-9802 19 41026611 41027335 −18.18 1.82 1.24E−04 9.91E−03 0.45 0.55 703 chr19-26240 19 58232322 58233098 −18.00 1.77 1.26E−04 1.00E−02 0.64 0.36 704 chr22-14234 22 43784756 43785475 −22.13 3.61 1.31E−04 1.04E−02 0.39 0.61 705 chr9-25922 9 98679294 98679924 −17.17 1.69 1.35E−04 1.06E−02 0.74 0.26 706 chr3-2993 3 149898092 149899204 −18.66 1.87 1.35E−04 1.06E−02 0.44 0.56 707 chr16-3077 16 47868513 47870198 −17.66 1.68 1.37E−04 1.08E−02 0.53 0.47 708 chr1-45365 1 81652725 81653024 −20.73 2.45 1.39E−04 1.09E−02 0.00 0.00 709 chr8-21185 8 142446426 142446725 −35.31 29.41 1.41E−04 1.10E−02 0.00 0.00 710 chr17-22033 17 44159065 44159578 −17.98 1.71 1.41E−04 1.10E−02 0.41 0.59 711 chr19-46114 19 54337512 54338223 −18.61 1.99 1.45E−04 1.13E−02 0.30 0.70 712 chr11-24196 11 32410931 32413157 −16.92 1.64 1.51E−04 1.17E−02 0.77 0.23 713 chr7-18563 7 2527436 2527812 −21.86 3.98 1.52E−04 1.17E−02 0.00 1.00 714 chr19-31045 19 22396428 22397198 −19.11 1.94 1.56E−04 1.20E−02 0.00 0.00 715 chr13-13783 13 27263290 27268058 −15.85 1.58 1.57E−04 1.21E−02 0.46 0.54 716 chr16-10570 16 19991910 19993697 −18.29 1.84 1.58E−04 1.22E−02 0.33 0.67 717 chr8-17537 8 23615270 23616687 −18.04 1.80 1.61E−04 1.23E−02 0.53 0.47 718 chr1-11022 1 26360354 26360829 −20.03 2.19 1.63E−04 1.25E−02 0.91 0.09 719 chr3-10449 3 148557618 148558026 −18.75 1.73 1.67E−04 1.27E−02 0.00 0.55 720 chr3-19059 3 9568824 9569550 −18.92 1.96 1.71E−04 1.30E−02 0.36 0.64 721 chr19-35922 19 55520704 55521725 −19.73 2.27 1.72E−04 1.30E−02 0.32 0.68 722 chr8-26677 8 141676464 141676763 −22.12 3.69 1.76E−04 1.33E−02 0.00 0.00 723 chr11-27829 11 22318966 22320078 −17.37 1.63 1.85E−04 1.38E−02 0.46 0.54 724 chr14-8228 14 21074297 21075104 −19.09 1.92 1.85E−04 1.38E−02 0.00 0.00 725 chr8-4443 8 24826766 24828794 −15.79 1.58 1.86E−04 1.39E−02 0.81 0.19 726 chr7-24476 7 127047249 127047992 −21.36 3.69 1.87E−04 1.39E−02 0.00 0.00 727 chr8-19833 8 143356659 143356958 −19.31 1.87 1.89E−04 1.41E−02 0.00 1.00 728 chr11-12951 11 6904062 6904897 −19.03 1.83 1.93E−04 1.44E−02 0.83 0.17 729 chr10-3216 10 79066063 79067359 −19.57 2.31 1.99E−04 1.48E−02 0.97 0.03 730 chr8-18358 8 144588313 144589359 −16.73 1.60 2.03E−04 1.50E−02 0.00 0.00 731 chr16-31613 16 83728346 83728886 −22.62 4.58 2.04E−04 1.51E−02 0.56 0.44 732 chr3-2228 3 148621006 148622296 −16.18 1.56 2.06E−04 1.52E−02 0.50 0.50 733 chr2-9342 2 176688986 176689871 −17.28 1.65 2.08E−04 1.53E−02 0.74 0.26 734 chr6-10441 6 27358632 27360245 −16.76 1.58 2.21E−04 1.61E−02 0.00 0.00 735 chr6-22508 6 29628870 29629928 −15.88 1.56 2.37E−04 1.71E−02 0.68 0.32 736 chr10-11960 10 112421814 112422487 −21.86 3.04 2.37E−04 1.71E−02 0.88 0.12 737 chr8-25433 8 124242036 124243002 −18.08 1.76 2.41E−04 1.73E−02 0.71 0.29 738 chr8-23865 8 143710429 143711017 −20.82 2.81 2.47E−04 1.77E−02 0.00 0.00 739 chr1-38653 1 63556809 63559083 −15.73 1.52 2.48E−04 1.77E−02 1.00 0.00 740 chr8-27810 8 145468821 145469864 −17.74 1.68 2.48E−04 1.77E−02 0.25 0.75 741 chr9-23405 9 95749855 95750434 −21.73 3.25 2.52E−04 1.79E−02 0.00 1.00 742 chr13-8897 13 42464009 42464693 −18.00 1.66 2.52E−04 1.79E−02 0.63 0.37 743 chr17-10321 17 72893521 72893820 −18.78 1.73 2.53E−04 1.80E−02 0.00 0.00 744 chr1-4856 1 103346188 103347175 −17.42 1.63 2.58E−04 1.83E−02 0.00 0.00 745 chr6-14517 6 35587318 35587692 −18.17 1.76 2.60E−04 1.84E−02 0.77 0.23 746 chr6-6486 6 28888677 28889053 −19.06 1.76 2.63E−04 1.86E−02 0.00 1.00 747 chr6-9437 6 43720677 43721056 −17.67 1.72 2.74E−04 1.92E−02 0.96 0.04 748 chr10-27233 10 101280559 101281416 −17.97 1.71 2.74E−04 1.92E−02 0.64 0.36 749 chr17-18721 17 72933896 72934270 −18.96 1.73 2.75E−04 1.92E−02 0.00 0.00 750 chr11-17573 11 68822506 68822892 −22.62 4.53 2.76E−04 1.93E−02 0.00 0.00 751 chr12-9653 12 106821502 106821877 −35.42 29.20 2.85E−04 1.98E−02 0.84 0.16 752 chr22-23171 22 22882253 22883195 −35.41 29.21 2.85E−04 1.98E−02 0.47 0.53 753 chr10-10203 10 101279569 101280477 −18.01 1.65 2.96E−04 2.05E−02 0.34 0.66 754 chr16-17571 16 9014474 9015029 −18.61 1.73 2.99E−04 2.06E−02 0.00 0.00 755 chr13-14820 13 97926663 97927038 −20.85 2.59 3.07E−04 2.11E−02 0.00 1.00 756 chr8-5741 8 56919297 56919906 −20.83 2.52 3.07E−04 2.11E−02 0.00 0.00 757 chr11-34050 11 122355856 122356563 −21.30 3.12 3.12E−04 2.14E−02 0.00 1.00 758 chr3-15060 3 66931170 66931701 −18.49 1.70 3.14E−04 2.15E−02 0.00 0.00 759 chr9-26649 9 99656732 99657696 −16.31 1.52 3.14E−04 2.15E−02 0.62 0.38 760 chr13-7948 13 27396809 27397199 −19.27 1.89 3.17E−04 2.17E−02 0.61 0.39 761 chr14-5903 14 76669824 76670283 −35.45 29.14 3.42E−04 2.30E−02 0.00 0.00 762 chr18-8094 18 10443675 10444423 −18.00 1.64 3.46E−04 2.33E−02 0.29 0.71 763 chr12-16201 12 109957155 109958601 −20.97 3.02 3.58E−04 2.39E−02 0.54 0.46 764 chr8-16621 8 145718161 145719871 −15.79 1.51 3.58E−04 2.39E−02 0.69 0.31 765 chr8-15516 8 65446926 65447225 −19.85 1.86 3.61E−04 2.40E−02 0.00 1.00 766 chr1-17428 1 90950424 90950882 −19.24 1.72 3.68E−04 2.45E−02 0.00 1.00 767 chr18-11487 18 53170434 53170733 −20.60 2.50 3.71E−04 2.46E−02 0.09 0.91 768 chr11-28585 11 108798003 108800346 −17.39 1.54 3.76E−04 2.48E−02 0.62 0.38 769 chr7-18939 7 150573094 150573546 −22.19 3.44 3.74E−04 2.48E−02 0.68 0.32 770 chr7-2510 7 4888353 4889381 −18.99 1.89 3.75E−04 2.48E−02 0.14 0.86 771 chr7-11427 7 86812365 86813417 −21.60 3.31 3.92E−04 2.57E−02 0.33 0.67 772 chr11-30995 11 132319281 132320002 −18.72 1.68 4.01E−04 2.62E−02 0.45 0.55 773 chr19-49418 19 59172707 59173789 −18.02 1.67 4.07E−04 2.66E−02 0.50 0.50 774 chr6-19743 6 117692358 117693371 −19.90 2.15 4.10E−04 2.67E−02 0.19 0.81 775 chr8-8451 8 139995655 139996047 −35.41 29.21 4.11E−04 2.67E−02 0.00 0.00 776 chr16-27356 16 47873886 47875016 −18.78 1.84 4.16E−04 2.70E−02 0.78 0.22 777 chr2-4712 2 182250696 182252163 −17.26 1.52 4.16E−04 2.70E−02 0.00 0.00 778 chr11-6782 11 46339498 46339953 −20.09 2.09 4.16E−04 2.70E−02 0.00 0.00 779 chr10-20530 10 50557375 50557969 −17.66 1.56 4.19E−04 2.72E−02 0.50 0.50 780 chr11-12271 11 46804641 46805127 −20.75 2.54 4.32E−04 2.79E−02 0.00 0.00 781 chr11-29093 11 17673651 17673950 −21.19 2.72 4.33E−04 2.79E−02 0.00 1.00 782 chr12-2463 12 125776431 125778625 −16.35 1.49 4.34E−04 2.80E−02 0.51 0.49 783 chr9-26765 9 137117535 137119363 −19.16 1.76 4.35E−04 2.81E−02 0.39 0.61 784 chr19-41652 19 22507612 22507911 −21.33 2.63 4.42E−04 2.83E−02 0.00 1.00 785 chr16-39788 16 29982494 29983133 −19.75 2.07 4.59E−04 2.92E−02 0.00 1.00 786 chr18-249 18 43028812 43029683 −16.12 1.48 4.60E−04 2.93E−02 0.88 0.12 787 chr3-6105 3 148588462 148589450 −16.94 1.48 4.67E−04 2.96E−02 0.00 0.25 788 chr7-19417 7 86110997 86112738 −18.09 1.58 4.79E−04 3.03E−02 0.00 0.00 789 chr2-4092 2 124498663 124500373 −17.23 1.48 4.87E−04 3.07E−02 0.59 0.41 790 chr1-10651 1 196155374 196156447 −16.55 1.48 4.92E−04 3.09E−02 0.00 1.00 791 chr11-2782 11 118797889 118798265 −22.08 3.59 4.92E−04 3.09E−02 0.54 0.46 792 chr5-172 5 6502005 6502716 −21.55 2.79 4.92E−04 3.09E−02 0.88 0.12 793 chr13-10897 13 57103538 57107350 −14.63 1.43 4.98E−04 3.12E−02 0.63 0.37 794 chr9-20659 9 21959647 21959946 −21.30 2.72 4.99E−04 3.12E−02 0.00 1.00 795 chr16-33015 16 1357709 1358087 −18.58 1.60 5.06E−04 3.15E−02 0.00 0.00 796 chr10-13508 10 50646183 50646772 −17.61 1.55 5.21E−04 3.23E−02 0.71 0.29 797 chr6-10868 6 44000049 44000348 −20.20 1.86 5.27E−04 3.26E−02 0.00 0.00 798 chr18-3706 18 33319509 33319999 −21.33 3.06 5.41E−04 3.33E−02 0.08 0.92 799 chr18-5056 18 72336354 72336986 −21.23 2.60 5.41E−04 3.33E−02 1.00 0.00 800 chr1-10203 1 21773561 21774294 −19.42 1.75 5.45E−04 3.35E−02 0.00 0.00 801 chr17-29531 17 44073871 44075510 −16.40 1.45 5.57E−04 3.42E−02 0.53 0.47 802 chr19-39407 19 52434946 52435401 −22.06 3.64 5.64E−04 3.45E−02 0.10 0.90 803 chr8-29731 8 145092002 145092688 −17.63 1.53 5.69E−04 3.47E−02 0.00 1.00 804 chr6-25261 6 27755606 27757375 −16.08 1.43 5.92E−04 3.59E−02 0.12 0.88 805 chr12-15097 12 61115300 61115599 −35.48 29.06 5.94E−04 3.60E−02 0.00 0.00 806 chr7-36853 7 92299813 92302446 −20.57 2.66 6.13E−04 3.69E−02 0.88 0.12 807 chr8-8438 8 38391353 38391733 −18.16 1.51 6.16E−04 3.71E−02 0.00 0.00 808 chr1-46192 1 199775112 199775968 −21.94 3.80 6.38E−04 3.82E−02 0.97 0.03 809 chr1-18402 1 110474178 110475057 −17.88 1.53 6.41E−04 3.83E−02 0.60 0.40 810 chr11-24335 11 74119235 74120026 −19.35 1.91 6.51E−04 3.88E−02 0.15 0.85 811 chr5-5052 5 63291787 63293760 −16.39 1.44 6.72E−04 3.99E−02 0.68 0.32 812 chr9-19431 9 66083242 66083541 −18.87 1.68 6.74E−04 4.00E−02 0.73 0.27 813 chr22-25789 22 44038255 44038641 −20.65 2.49 6.88E−04 4.07E−02 0.00 0.00 814 chr1-21514 1 26423939 26424890 −17.43 1.51 7.52E−04 4.39E−02 0.71 0.29 815 chr4-2115 4 5944225 5945132 −18.41 1.57 7.61E−04 4.43E−02 0.18 0.82 816 chr10-8149 10 44108655 44109163 −18.27 1.53 7.79E−04 4.51E−02 0.61 0.39 817 chr11-4047 11 32413697 32415714 −18.12 1.63 7.97E−04 4.60E−02 0.09 0.91 818 chr16-28483 16 60625710 60628552 −17.03 1.43 8.05E−04 4.64E−02 0.53 0.47 819 chr10-14177 10 102885581 102886942 −17.02 1.45 8.36E−04 4.77E−02 0.24 0.76 820 chr2-9331 2 176653510 176657430 −15.22 1.36 8.77E−04 4.95E−02 0.81 0.19 821 chr9-17475 9 94922180 94922561 −19.55 1.78 8.77E−04 4.95E−02 0.00 0.00 822 chr8-25100 8 145530028 145530950 −17.82 1.56 8.88E−04 4.99E−02 1.00 0.00 823 chr1-46896 1 9522331 9523130 −20.78 −2.77 6.72E−07 1.22E−04 0.88 0.12 824 chr10-1806 10 121291154 121291996 −19.51 −2.30 9.54E−07 1.64E−04 0.63 0.37 825 chr6-7206 6 2968391 2969638 −18.35 −1.86 9.57E−07 1.65E−04 0.00 0.00 826 chr2-24638 2 237127007 237127306 −19.90 −1.93 3.47E−06 4.90E−04 0.00 0.00 827 chr1-22430 1 166675449 166675833 −21.89 −3.13 4.74E−06 6.32E−04 0.00 0.00 828 chr16-21721 16 5572196 5572575 −19.74 −1.94 6.34E−06 8.04E−04 0.00 0.00 829 chr16-41361 16 72135367 72135666 −21.14 −2.40 1.55E−05 1.70E−03 0.00 0.00 830 chr11-20041 11 129112952 129113251 −20.05 −1.97 1.56E−05 1.71E−03 0.00 0.00 831 chr11-33860 11 122375007 122375306 −19.99 −2.05 1.96E−05 2.08E−03 0.00 0.00 832 chr13-2085 13 35902497 35903099 −19.09 −1.80 2.19E−05 2.28E−03 0.00 1.00 833 chr12-24790 12 128204248 128204547 −20.63 −2.32 2.22E−05 2.31E−03 0.00 0.00 834 chr10-25116 10 129960630 129960929 −19.27 −1.77 2.53E−05 2.58E−03 0.00 0.00 835 chr13-14938 13 43118830 43119129 −19.94 −1.86 2.90E−05 2.91E−03 0.00 0.00 836 chr8-14802 8 3088897 3089196 −19.25 −1.78 2.92E−05 2.92E−03 0.00 0.00 837 chr16-34470 16 82084464 82084763 −19.72 −1.76 4.01E−05 3.83E−03 0.00 0.00 838 chr15-11755 15 90661465 90661764 −20.89 −2.30 4.34E−05 4.08E−03 0.00 0.00 839 chr2-36107 2 2033286 2033661 −19.64 −1.75 6.14E−05 5.46E−03 0.00 0.00 840 chr16-40307 16 49508539 49508838 −19.70 −1.81 6.45E−05 5.69E−03 0.00 0.00 841 chr15-17372 15 86294813 86295112 −19.75 −1.77 9.23E−05 7.74E−03 0.00 0.00 842 chr17-5864 17 9965973 9966347 −19.93 −1.73 1.41E−04 1.10E−02 0.00 0.00 843 chr11-34899 11 130368453 130368752 −21.20 −2.23 1.52E−04 1.17E−02 0.00 0.00 844 chr8-24061 8 9792346 9792645 −20.66 −1.97 1.53E−04 1.18E−02 0.00 1.00 845 chr20-8094 20 29283035 29283661 −15.86 −1.30 1.70E−04 1.29E−02 0.00 0.00 846 chr10-25783 10 38849376 38849756 −16.82 −1.33 1.84E−04 1.38E−02 0.00 0.00 847 chr12-12927 12 5712122 5712421 −19.61 −1.62 2.09E−04 1.54E−02 0.00 0.00 848 chr20-10013 20 59441617 59441916 −19.82 −1.66 3.14E−04 2.15E−02 0.00 0.00 849 chr10-30301 10 38910740 38911411 −15.69 −1.24 3.35E−04 2.27E−02 0.00 0.00 850 chr8-20311 8 23530788 23531087 −19.76 −1.61 3.66E−04 2.43E−02 0.00 0.00 851 chr4-23273 4 23439030 23439329 −19.78 −1.60 3.94E−04 2.58E−02 0.00 0.00 852 chr5-29351 5 68374685 68374984 −21.95 −2.87 4.40E−04 2.83E−02 0.00 0.00 853 chr5-18842 5 54562689 54563650 −19.70 −1.69 4.53E−04 2.89E−02 0.46 0.54 854 chr8-2027 8 4517707 4518006 −17.81 −1.31 4.67E−04 2.96E−02 0.00 0.00 855 chr17-27731 17 10433681 10433980 −20.21 −1.83 4.99E−04 3.12E−02 0.00 0.00 856 chr16-37866 16 6632034 6632333 −22.22 −2.79 5.05E−04 3.15E−02 0.00 0.00 857 chr14-12411 14 75867409 75867708 −20.27 −1.60 5.42E−04 3.34E−02 0.00 0.00 858 chr8-21088 8 2800314 2800783 −19.01 −1.38 6.66E−04 3.96E−02 0.00 0.00 859 chr21-14737 21 35345487 35345786 −20.00 −1.52 7.59E−04 4.42E−02 0.00 0.00 860 chr11-10804 11 258741 259538 −19.18 −1.38 8.00E−04 4.61E−02 0.00 0.00 861 chr6-18690 6 137778131 137778782 −19.99 −1.65 8.05E−04 4.64E−02 0.00 0.00 862 chr12-25577 12 130026153 130026452 −19.25 −1.41 8.30E−04 4.74E−02 0.00 0.00 863 chr8-26072 8 10928026 10928325 −18.71 −1.29 8.71E−04 4.92E−02 0.00 0.00 864 chr12-16106 12 129620053 129620352 −20.46 −1.81 8.80E−04 4.96E−02 0.00 0.00 865 chr2-27025 2 2494857 2495156 −20.40 −1.70 8.80E−04 4.96E−02 0.00 0.00 ChromHMM ChromHMM ChromHMM ChromHMM HM450K: Row RefSeq: % RefSeq: RefSeq: RefSeq: % HMEC: HMEC: HMEC: HMEC: number No. promoter % exon % intron intergenic % promoter % enhancer % insulator % polycomb of probes  1 1.00 0.00 0.00 0.00 1.00 0.00 0.00 0.00 2  2 0.91 0.00 0.00 0.09 0.84 0.00 0.03 0.13 8  3 0.81 0.17 0.76 0.00 0.51 0.00 0.00 0.49 4  4 1.00 0.00 0.00 0.00 0.70 0.30 0.00 0.00 3  5 1.00 0.00 0.00 0.00 1.00 0.00 0.00 0.00 7  6 0.88 0.77 0.83 0.00 0.69 0.31 0.00 0.00 13  7 0.00 0.32 1.00 0.00 0.91 0.09 0.00 0.00 46  8 0.05 0.05 0.95 0.00 1.00 0.00 0.00 0.00 3  9 1.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 4  10 0.62 0.32 0.12 0.00 0.55 0.45 0.00 0.00 15  11 0.45 0.31 0.34 0.00 0.86 0.00 0.00 0.00 5  12 0.00 0.00 0.00 1.00 0.43 0.00 0.00 0.57 3  13 0.33 0.12 0.64 0.00 0.73 0.27 0.00 0.00 13  14 0.25 0.32 0.51 0.00 1.00 0.00 0.00 0.00 12  15 0.78 0.29 0.00 0.00 0.54 0.00 0.00 0.46 27  16 0.32 0.00 0.00 0.68 0.67 0.33 0.00 0.00 4  17 0.00 0.34 1.00 0.00 1.00 0.00 0.00 0.00 3  18 0.64 0.16 0.19 0.04 0.54 0.00 0.00 0.46 20  19 1.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 24  20 0.00 0.00 0.00 1.00 0.87 0.00 0.00 0.13 7  21 0.00 0.00 1.00 0.00 1.00 0.00 0.00 0.00 2  22 0.03 0.12 0.88 0.00 1.00 0.00 0.00 0.00 2  23 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0.00 2  24 0.24 0.55 0.50 0.00 0.47 0.00 0.00 0.53 18  25 0.41 0.79 0.00 0.00 1.00 0.00 0.00 0.00 3  26 0.05 0.14 0.96 0.00 0.84 0.00 0.00 0.16 8  27 0.47 0.00 0.00 0.53 0.32 0.00 0.00 0.68 8  28 0.23 0.58 0.08 0.00 0.37 0.00 0.00 0.63 14  29 0.00 0.00 0.00 1.00 0.00 0.00 0.00 1.00 4  30 0.26 0.24 0.57 0.00 0.97 0.00 0.00 0.03 11  31 0.00 0.00 0.00 1.00 1.00 0.00 0.00 0.00 4  32 0.23 0.05 0.79 0.00 0.82 0.00 0.18 0.00 10  33 0.00 0.13 0.87 0.00 0.34 0.00 0.00 0.66 6  34 0.91 0.05 1.00 0.00 0.94 0.05 0.00 0.02 11  35 0.89 0.19 1.00 0.00 0.95 0.00 0.00 0.05 6  36 1.00 0.00 0.00 0.00 1.00 0.00 0.00 0.00 4  37 0.00 0.20 0.31 0.00 0.23 0.00 0.00 0.77 4  38 0.00 0.00 0.00 1.00 0.00 1.00 0.00 0.00 5  39 0.00 0.00 1.00 0.00 0.96 0.00 0.00 0.04 4  40 1.00 0.27 0.64 0.00 0.00 0.00 0.00 1.00 6  41 0.00 0.29 0.71 0.00 1.00 0.00 0.00 0.00 3  42 0.00 0.41 0.59 0.00 0.37 0.00 0.00 0.63 10  43 0.00 0.31 0.00 0.00 0.00 0.00 0.00 1.00 5  44 0.00 0.00 0.00 1.00 1.00 0.00 0.00 0.00 7  45 0.73 0.48 0.63 0.00 0.81 0.00 0.00 0.19 15  46 0.00 0.29 0.72 0.00 0.00 0.00 0.00 1.00 1  47 0.00 0.00 0.00 1.00 1.00 0.00 0.00 0.00 6  48 0.00 0.00 1.00 0.00 1.00 0.00 0.00 0.00 4  49 0.39 0.16 0.59 0.00 0.84 0.00 0.00 0.16 13  50 0.24 0.43 1.00 0.00 1.00 0.00 0.00 0.00 5  51 0.00 0.00 0.00 1.00 1.00 0.00 0.00 0.00 0  52 0.81 0.00 0.00 0.19 0.00 0.00 0.00 1.00 15  53 0.69 0.27 0.09 0.00 0.88 0.00 0.00 0.12 7  54 0.00 0.00 0.00 1.00 0.63 0.00 0.00 0.37 7  55 0.00 0.00 0.00 1.00 0.00 0.00 0.00 0.00 5  56 1.00 0.00 0.00 0.00 1.00 0.00 0.00 0.00 6  57 0.00 0.00 0.00 1.00 0.00 0.00 0.00 1.00 7  58 0.00 0.00 1.00 0.00 1.00 0.00 0.00 0.00 1  59 0.00 0.09 1.00 0.00 1.00 0.00 0.00 0.00 7  60 0.66 0.24 0.80 0.00 0.83 0.00 0.00 0.17 11  61 0.00 0.10 0.90 0.00 1.00 0.00 0.00 0.00 12  62 0.00 0.53 0.47 0.00 0.82 0.18 0.00 0.00 2  63 0.31 0.67 0.10 0.00 1.00 0.00 0.00 0.00 6  64 0.37 0.17 0.49 0.00 0.83 0.00 0.00 0.17 17  65 0.00 0.00 0.00 0.00 0.54 0.00 0.00 0.46 4  66 0.00 0.00 0.00 0.33 0.98 0.00 0.02 0.00 7  67 0.10 1.00 0.00 0.00 0.72 0.00 0.00 0.28 3  68 0.00 0.00 1.00 0.00 0.00 0.00 0.00 1.00 6  69 1.00 0.47 0.48 0.00 0.96 0.00 0.00 0.00 12  70 0.00 0.00 1.00 0.00 0.00 0.00 0.00 1.00 9  71 0.00 0.00 0.00 1.00 0.00 0.00 0.00 1.00 6  72 0.68 0.06 0.33 0.00 0.00 0.00 0.00 0.99 15  73 0.00 0.62 0.38 0.00 0.73 0.27 0.00 0.00 3  74 0.57 0.53 1.00 0.00 0.96 0.04 0.00 0.00 5  75 0.00 1.00 0.00 0.00 0.05 0.00 0.00 0.95 6  76 0.21 0.50 0.36 0.00 0.45 0.00 0.00 0.55 2  77 0.05 0.35 0.65 0.00 0.27 0.00 0.00 0.73 5  78 0.00 0.29 0.71 0.00 1.00 0.00 0.00 0.00 5  79 0.00 0.00 1.00 0.00 0.43 0.00 0.00 0.57 15  80 0.00 0.00 1.00 0.00 0.00 0.00 0.00 1.00 5  81 0.00 0.00 0.00 1.00 0.00 0.00 0.00 1.00 7  82 1.00 0.00 0.00 0.00 0.69 0.31 0.00 0.00 6  83 0.00 1.00 0.00 0.00 0.43 0.00 0.00 0.57 1  84 0.00 0.07 0.92 0.00 0.68 0.00 0.00 0.32 4  85 0.00 1.00 0.12 0.00 0.66 0.00 0.00 0.34 5  86 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0.00 1  87 0.01 0.24 0.76 0.00 0.65 0.00 0.00 0.35 7  88 0.00 0.00 1.00 0.00 0.50 0.00 0.00 0.50 4  89 0.00 0.77 0.23 0.00 0.00 0.00 0.00 1.00 5  90 0.00 0.00 0.00 1.00 0.75 0.00 0.00 0.25 3  91 0.13 0.20 0.72 0.00 0.50 0.00 0.00 0.50 18  92 1.00 0.47 0.76 0.00 0.00 0.00 0.00 1.00 0  93 0.00 0.13 0.87 0.00 0.97 0.00 0.00 0.03 10  94 0.00 0.58 0.42 0.00 0.96 0.00 0.00 0.00 8  95 0.12 0.97 0.00 0.00 1.00 0.00 0.00 0.00 6  96 0.00 0.18 0.82 0.00 0.22 0.00 0.00 0.78 9  97 0.00 0.00 0.00 1.00 0.63 0.37 0.00 0.00 7  98 0.00 0.00 0.00 1.00 0.00 0.00 0.00 1.00 6  99 0.00 0.02 0.98 0.00 1.00 0.00 0.00 0.00 4 100 0.53 0.00 0.00 0.47 1.00 0.00 0.00 0.00 2 101 0.00 0.00 0.00 1.00 0.58 0.00 0.00 0.42 5 102 0.00 0.00 0.00 1.00 0.00 0.00 0.00 1.00 7 103 0.00 0.00 0.00 1.00 0.48 0.00 0.00 0.52 5 104 0.00 0.00 0.00 1.00 0.82 0.00 0.00 0.18 3 105 0.00 0.00 0.00 1.00 0.00 1.00 0.00 0.00 6 106 0.93 0.13 0.00 0.00 0.00 0.00 0.00 1.00 13 107 0.00 0.00 0.00 1.00 0.00 0.00 0.00 1.00 13 108 0.28 0.43 0.47 0.00 1.00 0.00 0.00 0.00 5 109 0.74 0.33 0.00 0.00 1.00 0.00 0.00 0.00 13 110 0.00 0.00 0.00 1.00 0.00 0.00 0.00 1.00 4 111 0.00 0.00 1.00 0.00 0.00 0.00 0.00 1.00 2 112 0.00 0.35 0.65 0.00 0.00 0.00 0.00 1.00 8 113 0.00 0.41 0.58 0.00 0.00 0.00 0.00 1.00 4 114 0.03 0.15 0.85 0.00 0.24 0.00 0.00 0.76 16 115 0.00 0.00 0.00 0.14 0.58 0.00 0.00 0.42 4 116 0.22 0.92 0.00 0.00 0.78 0.00 0.00 0.22 6 117 0.30 0.44 1.00 0.00 0.71 0.00 0.00 0.29 8 118 0.00 0.00 0.00 1.00 1.00 0.00 0.00 0.00 3 119 0.49 0.36 0.20 0.00 0.00 0.00 0.00 1.00 10 120 0.07 0.14 0.86 0.00 0.24 0.00 0.00 0.76 5 121 0.00 0.42 0.58 0.00 0.83 0.00 0.00 0.17 6 122 0.00 0.04 0.96 0.00 0.00 0.00 0.00 1.00 3 123 0.00 0.52 0.27 0.00 0.45 0.00 0.00 0.55 12 124 0.34 0.30 0.13 0.25 0.00 0.00 0.00 1.00 28 125 0.00 0.00 0.00 1.00 0.96 0.00 0.00 0.04 8 126 0.00 0.41 0.58 0.00 0.46 0.00 0.00 0.54 7 127 0.34 0.20 0.62 0.00 1.00 0.00 0.00 0.00 6 128 0.00 1.00 0.00 0.00 0.00 0.00 0.00 1.00 1 129 0.00 0.00 0.00 1.00 0.26 0.69 0.00 0.00 8 130 0.47 0.19 1.00 0.00 0.25 0.75 0.00 0.00 4 131 0.47 0.29 0.28 0.00 0.52 0.00 0.00 0.48 8 132 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 7 133 0.46 0.00 0.00 0.54 0.64 0.00 0.00 0.36 5 134 0.00 0.00 0.00 1.00 0.96 0.04 0.00 0.00 1 135 0.00 0.24 0.76 0.00 0.00 0.00 0.00 0.00 5 136 0.00 0.00 0.00 1.00 0.72 0.00 0.00 0.28 3 137 0.00 0.78 0.22 0.00 0.75 0.00 0.00 0.25 9 138 0.36 0.37 0.36 0.00 1.00 0.00 0.00 0.00 13 139 1.00 0.00 0.00 0.00 0.62 0.00 0.00 0.38 7 140 0.03 0.26 0.74 0.00 0.00 0.00 0.00 1.00 9 141 0.16 0.27 0.64 0.00 1.00 0.00 0.00 0.00 5 142 0.00 0.00 1.00 0.00 0.00 0.00 0.00 1.00 2 143 0.00 0.00 0.00 1.00 0.00 0.00 0.00 1.00 2 144 0.00 0.00 0.00 1.00 0.58 0.00 0.00 0.42 8 145 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 5 146 0.22 0.32 0.53 0.00 1.00 0.00 0.00 0.00 12 147 0.00 0.44 1.00 0.00 0.67 0.00 0.00 0.33 6 148 0.22 0.08 0.00 0.00 0.00 0.00 0.00 1.00 11 149 0.00 0.68 1.00 0.00 0.00 1.00 0.00 0.00 4 150 0.00 0.00 0.00 0.00 0.63 0.00 0.00 0.37 8 151 0.34 0.26 0.57 0.00 0.00 0.00 0.00 0.00 6 152 0.62 0.48 0.00 0.00 0.98 0.02 0.00 0.00 24 153 0.00 0.00 0.00 1.00 0.15 0.85 0.00 0.00 3 154 0.10 0.28 0.66 0.00 0.44 0.00 0.00 0.56 19 155 0.00 0.91 0.04 0.00 0.80 0.20 0.00 0.00 8 156 0.99 0.07 0.05 0.00 0.72 0.21 0.00 0.00 7 157 0.00 0.00 0.00 1.00 0.00 0.00 0.00 1.00 4 158 0.00 0.59 0.40 0.00 1.00 0.00 0.00 0.00 2 159 0.59 0.19 0.33 0.00 0.87 0.00 0.00 0.13 14 160 1.00 0.00 0.00 0.00 0.05 0.95 0.00 0.00 6 161 0.15 0.06 0.94 0.00 0.04 0.00 0.00 0.96 4 162 1.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 5 163 1.00 0.00 0.00 0.00 1.00 0.00 0.00 0.00 2 164 0.00 0.00 0.00 1.00 0.66 0.00 0.00 0.34 5 165 0.00 0.00 0.00 1.00 0.00 0.00 0.00 1.00 3 166 0.00 1.00 0.00 0.00 1.00 0.00 0.00 0.00 2 167 0.34 0.40 0.35 0.00 0.82 0.00 0.00 0.18 9 168 0.51 0.70 0.38 0.00 1.00 0.00 0.00 0.00 6 169 0.00 0.00 0.00 1.00 0.72 0.19 0.00 0.00 6 170 0.31 0.15 0.60 0.00 0.89 0.00 0.00 0.11 6 171 0.00 0.00 0.00 1.00 0.37 0.18 0.00 0.00 8 172 0.00 0.00 0.00 1.00 0.84 0.00 0.00 0.16 1 173 0.00 0.00 0.00 0.62 0.84 0.16 0.00 0.00 7 174 0.49 0.35 0.22 0.00 0.56 0.00 0.00 0.44 10 175 0.00 0.00 1.00 0.00 0.89 0.00 0.00 0.11 4 176 0.00 0.00 0.00 1.00 0.00 0.00 0.00 0.00 4 177 0.11 0.60 0.35 0.00 0.36 0.00 0.00 0.64 11 178 1.00 0.00 0.00 0.00 0.76 0.00 0.00 0.24 9 179 0.66 0.00 1.00 0.00 0.63 0.00 0.00 0.37 6 180 0.00 0.00 1.00 0.00 0.00 0.00 0.00 1.00 2 181 0.00 0.75 0.24 0.00 0.75 0.25 0.00 0.00 2 182 0.00 0.00 0.00 1.00 0.76 0.00 0.00 0.24 7 183 0.00 0.00 1.00 0.00 0.41 0.00 0.00 0.59 10 184 0.40 0.00 0.00 0.60 0.11 0.00 0.00 0.89 20 185 1.00 0.16 0.84 0.00 1.00 0.00 0.00 0.00 5 186 0.00 0.00 1.00 0.00 1.00 0.00 0.00 0.00 3 187 0.39 0.71 1.00 0.00 1.00 0.00 0.00 0.00 14 188 0.00 0.52 0.48 0.00 0.41 0.00 0.00 0.59 3 189 0.00 0.00 1.00 0.00 1.00 0.00 0.00 0.00 5 190 0.48 0.09 0.88 0.00 0.95 0.00 0.00 0.05 9 191 0.00 0.00 0.00 1.00 0.00 0.00 0.00 1.00 2 192 0.00 0.00 0.00 1.00 1.00 0.00 0.00 0.00 3 193 0.00 0.00 0.00 1.00 0.82 0.18 0.00 0.00 3 194 0.00 0.15 0.85 0.00 0.76 0.00 0.00 0.24 2 195 1.00 0.16 0.24 0.00 1.00 0.00 0.00 0.00 6 196 0.27 0.68 0.11 0.00 0.93 0.00 0.00 0.07 15 197 0.72 0.32 0.00 0.00 0.35 0.00 0.00 0.65 16 198 0.68 0.15 0.92 0.00 0.87 0.00 0.00 0.13 4 199 0.00 0.25 0.75 0.00 0.91 0.00 0.00 0.09 5 200 0.86 0.11 0.07 0.00 0.49 0.00 0.00 0.51 14 201 0.90 0.12 0.05 0.00 0.66 0.00 0.00 0.34 11 202 0.00 0.00 1.00 0.00 0.00 0.00 0.00 1.00 0 203 0.10 0.18 0.78 0.00 0.48 0.00 0.00 0.52 10 204 0.49 0.23 0.42 0.00 0.50 0.50 0.00 0.00 10 205 0.89 0.12 0.09 0.00 0.00 0.89 0.00 0.00 12 206 1.00 0.00 0.00 0.00 0.35 0.00 0.00 0.65 3 207 1.00 0.30 0.70 0.00 1.00 0.00 0.00 0.00 2 208 0.00 0.00 1.00 0.00 0.00 0.00 0.00 1.00 8 209 0.00 0.00 0.00 0.61 0.00 0.00 0.00 1.00 6 210 0.30 0.15 0.61 0.00 0.95 0.05 0.00 0.00 3 211 0.00 0.00 0.00 1.00 0.00 0.00 0.00 0.00 2 212 0.00 0.00 1.00 0.00 0.00 0.72 0.00 0.28 2 213 0.65 0.15 0.29 0.00 0.72 0.00 0.00 0.28 13 214 0.00 0.00 0.00 1.00 0.00 0.00 0.00 1.00 5 215 0.59 0.46 0.00 0.00 1.00 0.00 0.00 0.00 12 216 1.00 0.48 0.14 0.00 0.90 0.10 0.00 0.00 29 217 0.36 0.28 0.42 0.00 0.39 0.00 0.00 0.61 13 218 0.64 0.34 0.08 0.00 0.96 0.00 0.00 0.04 11 219 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 1 220 0.25 0.36 0.43 0.00 0.00 0.00 0.00 1.00 11 221 1.00 0.00 1.00 0.00 1.00 0.00 0.00 0.00 3 222 0.00 0.55 0.45 0.00 0.98 0.00 0.00 0.02 5 223 0.00 0.00 0.00 1.00 0.32 0.00 0.00 0.68 5 224 0.18 0.43 1.00 0.00 0.00 0.00 0.00 1.00 3 225 0.68 0.06 0.40 0.00 0.97 0.03 0.00 0.00 9 226 0.00 0.00 0.00 1.00 0.49 0.00 0.00 0.51 3 227 0.00 0.00 0.00 1.00 1.00 0.00 0.00 0.00 4 228 0.00 0.00 1.00 0.00 0.00 0.00 0.00 1.00 4 229 0.42 0.19 0.41 0.00 0.00 0.00 0.00 1.00 21 230 0.00 0.48 0.52 0.00 0.34 0.00 0.00 0.66 7 231 0.00 0.00 0.00 1.00 0.00 0.00 0.00 1.00 10 232 1.00 0.00 0.00 0.00 1.00 0.00 0.00 0.00 2 233 0.63 0.34 0.71 0.00 0.63 0.00 0.00 0.37 12 234 0.00 0.00 1.00 0.00 0.84 0.00 0.00 0.16 5 235 0.94 0.21 0.00 0.00 0.97 0.03 0.00 0.00 5 236 1.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 6 237 0.00 0.00 0.00 1.00 0.00 0.00 0.00 1.00 1 238 0.13 0.47 0.48 0.00 0.42 0.00 0.00 0.58 4 239 0.00 0.59 0.41 0.00 1.00 0.00 0.00 0.00 9 240 0.00 0.00 0.00 1.00 1.00 0.00 0.00 0.00 3 241 0.00 0.00 0.00 1.00 0.63 0.00 0.00 0.37 12 242 0.00 0.00 1.00 0.00 0.00 1.00 0.00 0.00 2 243 0.82 0.24 0.00 0.00 0.91 0.00 0.00 0.09 6 244 0.53 0.63 0.57 0.00 0.19 0.00 0.00 0.81 14 245 0.00 0.73 0.26 0.00 0.58 0.42 0.00 0.00 4 246 0.77 0.19 1.00 0.00 1.00 0.00 0.00 0.00 6 247 1.00 0.00 0.00 0.00 0.99 0.01 0.00 0.00 3 248 0.11 0.49 0.51 0.00 0.00 0.00 0.00 1.00 6 249 0.00 0.00 0.00 0.88 0.00 0.00 0.00 1.00 3 250 0.00 0.00 0.00 1.00 0.00 0.00 0.00 1.00 2 251 0.00 0.00 1.00 0.00 0.37 0.00 0.00 0.63 18 252 0.00 0.00 0.00 1.00 0.76 0.00 0.00 0.24 8 253 0.00 0.00 0.00 1.00 0.77 0.00 0.00 0.23 21 254 0.00 0.50 0.50 0.00 0.00 0.00 0.00 1.00 2 255 0.00 0.32 0.68 0.00 0.31 0.00 0.00 0.69 4 256 0.56 0.40 0.15 0.00 0.82 0.00 0.00 0.18 2 257 0.00 0.00 1.00 0.00 0.45 0.00 0.00 0.55 15 258 0.52 0.40 0.11 0.00 0.99 0.00 0.00 0.01 13 259 0.58 0.00 0.00 0.42 0.00 0.00 0.00 1.00 9 260 0.00 0.00 0.00 1.00 0.00 0.00 0.00 1.00 1 261 0.36 0.29 0.38 0.00 0.97 0.03 0.00 0.00 14 262 0.00 0.00 0.00 1.00 0.00 0.00 0.00 1.00 3 263 0.00 0.00 0.00 1.00 0.00 0.00 0.00 1.00 6 264 0.00 0.00 1.00 0.00 0.64 0.00 0.00 0.36 2 265 1.00 0.00 0.00 0.00 0.80 0.00 0.00 0.20 3 266 0.40 0.00 0.00 0.60 0.00 0.00 0.00 1.00 2 267 0.33 0.50 0.00 0.00 0.00 0.00 0.00 1.00 10 268 0.00 0.00 0.00 1.00 0.00 0.00 0.00 0.75 2 269 0.00 0.27 0.72 0.00 0.58 0.00 0.00 0.42 3 270 0.44 0.39 0.49 0.00 1.00 0.00 0.00 0.00 1 271 0.00 0.00 0.00 1.00 0.00 0.00 0.00 1.00 11 272 0.07 0.10 0.88 0.00 0.27 0.00 0.00 0.73 11 273 0.52 0.27 0.38 0.00 0.98 0.02 0.00 0.00 7 274 0.14 0.31 0.62 0.00 0.16 0.00 0.00 0.84 5 275 0.13 0.16 0.84 0.00 1.00 0.00 0.00 0.00 5 276 1.00 0.77 0.23 0.00 0.00 0.00 0.00 1.00 3 277 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0.00 3 278 0.35 0.14 1.00 0.00 0.35 0.00 0.00 0.65 7 279 0.74 0.33 1.00 0.00 0.00 0.00 0.00 0.98 3 280 0.00 0.48 0.46 0.00 0.19 0.00 0.00 0.81 13 281 1.00 0.38 0.69 0.00 0.00 0.00 0.00 1.00 8 282 0.00 0.00 1.00 0.00 0.99 0.00 0.00 0.01 7 283 0.00 0.18 0.82 0.00 0.00 0.00 0.00 1.00 5 284 0.00 0.00 0.00 1.00 0.68 0.00 0.00 0.32 3 285 0.00 0.00 0.00 1.00 0.00 0.00 0.00 1.00 9 286 0.49 0.33 1.00 0.00 1.00 0.00 0.00 0.00 12 287 0.00 0.26 0.74 0.00 0.13 0.00 0.00 0.87 9 288 0.42 0.83 0.00 0.00 0.00 0.00 0.00 1.00 3 289 0.00 0.56 0.44 0.00 0.00 0.00 0.00 1.00 4 290 0.00 0.00 0.00 1.00 0.10 0.00 0.00 0.90 6 291 0.00 0.34 0.65 0.00 1.00 0.00 0.00 0.00 9 292 1.00 0.59 0.00 0.00 1.00 0.00 0.00 0.00 14 293 0.00 0.00 0.00 1.00 1.00 0.00 0.00 0.00 9 294 0.46 0.09 0.60 0.00 1.00 0.00 0.00 0.00 9 295 0.00 0.00 0.00 1.00 0.00 0.00 0.00 0.00 2 296 0.00 0.00 0.00 1.00 0.00 0.00 1.00 0.00 3 297 0.00 0.03 0.97 0.00 1.00 0.00 0.00 0.00 8 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0.00 0.00 0.68 6 320 0.82 0.44 0.00 0.00 0.75 0.00 0.00 0.00 6 321 1.00 0.00 0.00 0.00 0.00 0.82 0.00 0.00 2 322 0.25 0.29 0.57 0.00 0.00 0.00 0.00 1.00 10 323 0.00 1.00 0.65 0.00 0.00 0.00 0.00 1.00 6 324 0.88 0.21 0.00 0.00 0.52 0.00 0.00 0.48 11 325 0.00 0.00 0.00 1.00 0.00 0.00 0.00 1.00 7 326 0.00 0.00 0.00 1.00 0.18 0.00 0.00 0.82 0 327 0.00 0.00 0.00 1.00 0.00 0.25 0.00 0.00 3 328 0.00 0.00 1.00 0.00 0.99 0.00 0.00 0.01 3 329 1.00 0.00 0.00 0.00 0.79 0.00 0.00 0.21 4 330 0.68 0.12 0.36 0.00 0.82 0.00 0.00 0.18 4 331 0.00 0.30 0.56 0.00 0.00 0.76 0.00 0.00 5 332 0.00 0.00 0.00 1.00 0.96 0.00 0.00 0.04 4 333 0.00 0.00 0.00 1.00 0.00 0.00 0.00 1.00 1 334 1.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 3 335 0.00 0.00 1.00 0.00 1.00 0.00 0.00 0.00 5 336 0.00 0.00 0.00 1.00 0.00 0.00 0.00 1.00 5 337 0.78 0.37 0.05 0.00 1.00 0.00 0.00 0.00 5 338 0.00 0.00 1.00 0.00 0.00 0.00 0.00 1.00 5 339 0.00 0.00 1.00 0.00 0.00 1.00 0.00 0.00 1 340 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0.21 2 341 0.00 1.00 0.00 0.00 0.49 0.51 0.00 0.00 4 342 1.00 0.83 0.22 0.00 1.00 0.00 0.00 0.00 38 343 0.00 0.00 0.00 1.00 1.00 0.00 0.00 0.00 9 344 0.00 0.00 0.00 1.00 0.00 0.00 0.00 1.00 9 345 1.00 0.00 0.00 0.00 0.49 0.00 0.00 0.51 8 346 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 7 347 0.19 0.49 0.09 0.00 1.00 0.00 0.00 0.00 18 348 0.00 0.00 0.00 0.79 0.00 0.00 0.00 1.00 15 349 0.43 0.28 0.43 0.00 0.45 0.00 0.00 0.55 14 350 0.53 0.28 0.31 0.00 1.00 0.00 0.00 0.00 9 351 0.00 0.13 0.86 0.00 0.19 0.00 0.00 0.81 8 352 0.00 0.00 0.00 1.00 0.00 0.00 0.00 1.00 3 353 0.25 0.30 0.62 0.00 1.00 0.00 0.00 0.00 4 354 0.00 0.00 0.00 1.00 0.31 0.09 0.00 0.46 7 355 0.00 0.00 0.00 1.00 0.00 0.00 0.00 1.00 13 356 0.00 0.00 0.00 1.00 0.89 0.00 0.00 0.00 7 357 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 8 358 0.00 0.00 0.00 1.00 0.00 0.00 0.00 1.00 5 359 0.00 0.00 0.00 1.00 0.00 0.00 0.00 1.00 3 360 0.00 0.00 0.00 1.00 1.00 0.00 0.00 0.00 3 361 0.00 0.00 0.00 1.00 0.00 0.00 0.00 1.00 5 362 0.05 0.72 0.28 0.00 0.89 0.00 0.00 0.11 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0.00 0.19 0.81 0.00 1.00 0.00 0.00 0.00 4 407 0.00 0.54 0.45 0.00 0.00 1.00 0.00 0.00 2 408 0.00 0.00 1.00 0.00 1.00 0.00 0.00 0.00 5 409 0.00 0.00 0.00 0.00 0.98 0.02 0.00 0.00 6 410 0.00 0.89 0.11 0.00 0.00 0.00 0.00 1.00 6 411 0.00 0.00 1.00 0.00 0.63 0.00 0.00 0.37 11 412 0.00 1.00 0.00 0.00 0.00 0.00 0.00 1.00 4 413 0.39 0.50 0.61 0.00 1.00 0.00 0.00 0.00 11 414 0.00 0.00 0.00 1.00 1.00 0.00 0.00 0.00 1 415 1.00 0.00 0.00 0.00 0.56 0.00 0.00 0.44 6 416 0.00 0.71 0.29 0.00 0.11 0.00 0.00 0.89 10 417 0.00 0.00 0.00 1.00 0.00 0.00 0.00 1.00 10 418 0.26 0.83 0.00 0.00 1.00 0.00 0.00 0.00 3 419 0.17 0.28 0.60 0.00 0.61 0.39 0.00 0.00 11 420 0.00 0.00 0.00 0.19 0.00 0.00 0.00 1.00 7 421 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 0 422 0.00 0.00 1.00 0.00 0.81 0.00 0.00 0.19 1 423 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0.82 16 424 0.00 0.00 0.00 1.00 0.72 0.00 0.00 0.28 9 425 0.00 0.53 0.47 0.00 0.86 0.00 0.00 0.14 4 426 0.00 0.00 0.00 1.00 0.00 0.00 0.00 1.00 4 427 0.00 0.00 0.00 1.00 1.00 0.00 0.00 0.00 3 428 0.39 0.14 0.53 0.00 0.00 0.00 0.00 0.25 11 429 0.90 0.10 0.08 0.00 0.68 0.18 0.00 0.00 10 430 0.54 0.64 0.39 0.00 0.64 0.00 0.00 0.36 13 431 0.00 0.00 0.00 1.00 0.00 0.00 0.00 1.00 4 432 0.00 0.00 0.00 1.00 0.00 0.83 0.00 0.00 4 433 0.00 0.00 0.00 1.00 0.50 0.00 0.00 0.50 4 434 0.00 0.00 1.00 0.00 0.46 0.00 0.00 0.54 6 435 0.00 0.00 1.00 0.00 0.00 1.00 0.00 0.00 4 436 0.22 0.74 0.18 0.00 0.85 0.00 0.00 0.15 5 437 0.00 0.00 1.00 0.00 0.00 0.00 0.00 1.00 8 438 0.42 0.26 0.48 0.00 0.81 0.00 0.00 0.19 5 439 1.00 0.00 1.00 0.00 0.00 0.00 0.00 1.00 3 440 0.21 0.89 0.00 0.00 0.99 0.00 0.00 0.01 5 441 0.00 0.00 1.00 0.00 0.00 0.00 0.00 1.00 4 442 0.34 0.28 0.51 0.00 1.00 0.00 0.00 0.00 9 443 0.00 0.00 0.00 1.00 0.00 0.87 0.13 0.00 4 444 0.77 0.00 0.00 0.23 1.00 0.00 0.00 0.00 10 445 0.00 0.00 0.00 1.00 0.25 0.00 0.00 0.75 7 446 0.00 0.00 0.00 1.00 0.00 0.00 0.00 1.00 6 447 0.79 0.37 0.00 0.00 1.00 0.00 0.00 0.00 8 448 0.01 0.18 0.82 0.00 0.00 0.00 0.00 1.00 5 449 0.00 0.00 0.00 1.00 0.00 0.00 0.00 1.00 2 450 0.00 0.72 0.27 0.00 0.00 1.00 0.00 0.00 4 451 0.00 0.63 0.37 0.00 0.00 0.00 0.00 1.00 4 452 0.72 0.97 1.00 0.00 0.60 0.00 0.00 0.40 7 453 0.94 0.14 0.00 0.00 0.12 0.00 0.00 0.88 8 454 0.34 0.21 0.59 0.00 1.00 0.00 0.00 0.00 7 455 0.47 0.51 0.14 0.00 1.00 0.00 0.00 0.00 8 456 0.00 0.00 1.00 0.00 0.91 0.09 0.00 0.00 4 457 0.24 0.36 0.59 0.00 0.93 0.07 0.00 0.00 6 458 1.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 7 459 1.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 5 460 0.64 0.33 0.08 0.00 0.43 0.00 0.00 0.57 14 461 1.00 0.27 0.00 0.00 0.95 0.00 0.00 0.05 15 462 0.55 0.33 0.66 0.00 0.41 0.40 0.00 0.19 7 463 0.00 0.00 0.00 1.00 1.00 0.00 0.00 0.00 6 464 0.52 0.16 0.36 0.00 0.00 0.00 0.00 0.00 11 465 0.23 0.85 0.00 0.00 0.60 0.00 0.00 0.40 5 466 0.00 0.00 0.00 1.00 0.00 0.00 0.00 1.00 0 467 0.00 0.00 0.00 1.00 0.00 0.00 0.00 1.00 4 468 0.00 0.00 0.00 1.00 0.76 0.24 0.00 0.00 5 469 1.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 4 470 0.00 0.00 1.00 0.00 0.00 0.00 0.76 0.07 4 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0.00 1.00 4 493 0.00 0.20 0.80 0.00 0.28 0.00 0.00 0.72 6 494 0.00 0.00 0.00 1.00 0.00 0.00 0.00 1.00 8 495 0.74 0.00 0.00 0.26 0.16 0.00 0.00 0.84 9 496 0.00 0.00 0.00 1.00 0.00 0.00 0.00 1.00 2 497 0.45 0.00 0.00 0.55 0.00 0.00 0.00 1.00 3 498 0.37 0.43 0.30 0.00 0.75 0.00 0.00 0.25 8 499 0.16 0.41 0.54 0.00 0.83 0.17 0.00 0.00 11 500 0.00 0.00 1.00 0.00 0.00 0.00 0.00 1.00 5 501 0.00 0.48 0.52 0.00 0.00 0.00 0.00 1.00 0 502 0.00 0.00 0.00 1.00 0.00 0.00 0.00 1.00 3 503 0.41 0.15 0.53 0.00 0.00 0.00 0.00 1.00 7 504 1.00 0.00 0.00 0.00 1.00 0.00 0.00 0.00 1 505 1.00 0.29 0.71 0.00 0.76 0.00 0.00 0.24 5 506 0.32 0.09 0.68 0.00 0.69 0.00 0.00 0.31 8 507 0.00 0.49 0.51 0.00 0.72 0.00 0.00 0.28 7 508 0.18 0.34 0.55 0.00 0.91 0.00 0.00 0.09 8 509 0.00 0.00 1.00 0.00 0.00 0.00 0.00 1.00 2 510 0.35 0.76 0.05 0.00 0.00 0.00 0.00 1.00 5 511 0.00 0.30 0.70 0.00 0.00 0.00 0.00 0.00 5 512 0.00 0.00 0.00 1.00 0.63 0.00 0.00 0.37 3 513 0.00 0.00 0.00 1.00 0.00 0.00 0.00 1.00 3 514 0.98 0.10 0.30 0.00 0.31 0.69 0.00 0.00 2 515 0.54 0.14 0.47 0.00 0.59 0.41 0.00 0.00 9 516 0.33 0.45 0.12 0.00 0.15 0.00 0.00 0.85 12 517 0.00 0.00 0.00 1.00 1.00 0.00 0.00 0.00 3 518 0.00 1.00 0.00 0.00 0.00 0.00 0.00 1.00 4 519 0.00 0.00 0.00 1.00 0.42 0.00 0.00 0.58 9 520 1.00 0.00 0.00 0.00 0.29 0.00 0.00 0.71 2 521 0.52 0.59 0.00 0.00 0.80 0.20 0.00 0.00 7 522 0.00 0.00 0.00 1.00 0.00 0.00 0.00 1.00 2 523 0.00 0.00 0.00 1.00 0.00 0.00 0.00 1.00 4 524 0.00 0.00 0.00 1.00 0.00 0.00 0.00 1.00 2 525 0.77 0.23 0.07 0.00 0.61 0.00 0.00 0.39 4 526 0.15 0.68 0.93 0.00 1.00 0.00 0.00 0.00 3 527 0.31 0.24 0.76 0.00 0.00 1.00 0.00 0.00 0 528 0.00 1.00 0.00 0.00 0.00 0.00 0.00 1.00 10 529 0.00 0.39 0.61 0.00 0.00 0.00 0.00 1.00 6 530 1.00 0.00 0.00 0.00 1.00 0.00 0.00 0.00 3 531 1.00 0.00 0.00 0.00 0.59 0.41 0.00 0.00 1 532 0.00 0.00 0.00 1.00 0.82 0.00 0.00 0.18 9 533 0.00 0.00 1.00 0.00 0.95 0.00 0.00 0.05 9 534 0.00 0.00 0.00 1.00 0.46 0.00 0.00 0.54 7 535 0.00 0.00 1.00 0.00 0.00 0.00 0.00 1.00 2 536 0.00 0.00 1.00 0.00 0.00 0.00 0.00 1.00 6 537 0.69 0.07 0.29 0.00 0.00 0.00 0.00 0.87 11 538 0.00 0.00 0.00 1.00 0.00 0.00 0.00 0.00 6 539 0.00 0.00 0.00 1.00 0.64 0.00 0.00 0.36 1 540 0.34 0.01 0.70 0.00 0.67 0.00 0.00 0.33 8 541 0.00 0.00 1.00 0.00 0.00 0.00 0.00 1.00 3 542 0.18 0.67 0.25 0.00 0.00 0.00 0.00 1.00 9 543 0.00 0.00 0.00 1.00 0.00 0.00 0.00 0.00 1 544 0.00 0.00 1.00 0.00 1.00 0.00 0.00 0.00 1 545 1.00 0.00 0.00 0.00 0.85 0.00 0.00 0.15 9 546 0.00 0.00 0.00 1.00 1.00 0.00 0.00 0.00 10 547 0.00 0.00 0.00 1.00 0.66 0.00 0.00 0.34 6 548 0.44 0.26 0.46 0.00 1.00 0.00 0.00 0.00 5 549 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 4 550 0.00 0.00 0.00 1.00 0.00 0.00 0.00 1.00 3 551 0.00 0.15 0.85 0.00 1.00 0.00 0.00 0.00 8 552 0.31 0.14 0.62 0.00 0.00 0.00 0.00 1.00 10 553 0.00 0.00 0.00 1.00 0.71 0.00 0.00 0.29 10 554 0.00 0.00 0.00 1.00 0.81 0.00 0.00 0.19 8 555 0.00 0.00 1.00 0.00 0.83 0.00 0.00 0.17 4 556 0.00 1.00 0.00 0.00 0.83 0.00 0.00 0.17 6 557 0.00 0.34 0.66 0.00 0.00 0.00 0.00 1.00 4 558 0.00 0.61 0.39 0.00 0.88 0.00 0.00 0.12 6 559 0.00 0.00 0.00 1.00 0.00 0.00 0.00 1.00 5 560 0.00 0.00 1.00 0.00 1.00 0.00 0.00 0.00 3 561 0.02 0.01 0.99 0.00 0.00 0.00 0.00 0.49 7 562 0.84 0.21 0.00 0.00 0.00 0.00 0.00 1.00 7 563 0.43 0.15 0.50 0.00 1.00 0.00 0.00 0.00 11 564 1.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 4 565 0.00 0.00 0.00 1.00 0.00 0.00 0.00 1.00 0 566 0.00 0.31 0.73 0.00 0.00 0.00 0.00 1.00 1 567 1.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 3 568 0.46 0.16 0.55 0.00 1.00 0.00 0.00 0.00 8 569 0.00 0.00 0.00 1.00 0.63 0.00 0.00 0.37 2 570 0.00 0.00 0.00 1.00 0.80 0.15 0.00 0.00 7 571 1.00 0.00 0.00 0.00 0.85 0.00 0.00 0.15 0 572 0.00 0.00 0.00 1.00 0.00 0.00 0.00 1.00 2 573 0.00 0.00 0.00 1.00 0.00 0.00 0.00 1.00 6 574 0.53 0.56 1.00 0.00 0.00 1.00 0.00 0.00 13 575 0.43 0.09 0.51 0.00 0.27 0.00 0.00 0.73 16 576 0.27 0.19 0.79 0.00 0.19 0.00 0.00 0.81 21 577 0.75 0.15 0.15 0.00 0.00 0.00 0.00 1.00 22 578 0.44 0.38 0.22 0.00 0.00 0.00 0.00 1.00 10 579 0.49 0.52 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0.00 0.00 0.00 1.00 0.00 0.00 0.67 0.00 5 667 0.00 0.00 0.00 1.00 0.00 0.00 0.00 1.00 1 668 0.00 0.00 0.00 1.00 0.00 0.29 0.29 0.00 4 669 0.00 0.00 0.00 1.00 1.00 0.00 0.00 0.00 15 670 1.00 0.00 0.00 0.00 0.83 0.00 0.00 0.17 4 671 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 4 672 0.00 1.00 0.00 0.00 1.00 0.00 0.00 0.00 3 673 0.31 0.74 0.00 0.00 0.96 0.00 0.00 0.04 10 674 1.00 0.05 0.00 0.00 0.75 0.20 0.00 0.00 7 675 0.55 0.53 1.00 0.00 0.65 0.35 0.00 0.00 14 676 0.24 0.00 1.00 0.00 0.00 0.00 0.00 1.00 8 677 0.00 0.46 1.00 0.00 0.00 0.00 0.00 1.00 5 678 0.00 0.05 0.95 0.00 0.23 0.00 0.00 0.77 22 679 0.00 0.00 0.00 1.00 1.00 0.00 0.00 0.00 4 680 0.35 0.53 0.31 0.00 1.00 0.00 0.00 0.00 6 681 0.59 0.16 0.40 0.00 1.00 0.00 0.00 0.00 9 682 0.00 0.56 0.44 0.00 0.00 1.00 0.00 0.00 1 683 0.00 0.00 0.00 1.00 0.00 1.00 0.00 0.00 4 684 0.00 0.00 1.00 0.00 0.00 1.00 0.00 0.00 0 685 0.00 0.00 0.00 0.00 0.00 0.00 0.97 0.00 0 686 0.33 0.21 0.54 0.00 1.00 0.00 0.00 0.00 11 687 0.00 0.00 0.00 1.00 0.44 0.58 0.00 0.00 4 688 0.00 0.00 0.00 1.00 0.00 1.00 0.00 0.00 3 689 1.00 0.41 0.00 0.00 0.00 0.73 0.00 0.00 3 690 0.00 0.00 0.00 1.00 0.00 0.00 0.00 1.00 4 691 0.00 0.00 0.00 1.00 0.63 0.00 0.00 0.37 2 692 0.00 0.00 0.00 1.00 0.00 0.00 0.95 0.00 0 693 0.00 0.24 0.76 0.00 0.00 0.00 0.00 1.00 5 694 0.00 0.00 1.00 0.00 0.00 0.00 0.00 1.00 8 695 1.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 3 696 0.00 0.00 1.00 0.00 1.00 0.00 0.00 0.00 4 697 0.00 0.00 0.00 1.00 0.00 0.00 0.00 1.00 0 698 0.27 0.09 0.72 0.00 0.00 0.00 0.00 1.00 5 699 1.00 0.00 0.00 0.00 1.00 0.00 0.00 0.00 10 700 0.30 0.78 1.00 0.00 0.00 1.00 0.00 0.00 8 701 0.00 0.00 0.00 1.00 1.00 0.00 0.00 0.00 5 702 0.00 0.39 0.61 0.00 0.00 0.00 0.00 1.00 3 703 0.00 0.00 0.00 1.00 0.77 0.23 0.00 0.00 3 704 1.00 0.00 0.00 0.00 0.90 0.00 0.00 0.10 4 705 0.00 0.00 0.00 1.00 0.00 0.80 0.00 0.00 2 706 0.32 0.23 0.54 0.00 0.64 0.00 0.00 0.36 8 707 0.00 0.51 0.00 0.00 0.00 0.00 0.00 1.00 10 708 0.00 0.00 0.00 1.00 0.00 0.00 0.92 0.00 0 709 0.92 0.21 0.19 0.00 0.00 1.00 0.00 0.00 5 710 0.00 0.66 0.34 0.00 0.00 0.00 0.00 1.00 4 711 0.00 0.28 0.72 0.00 0.59 0.41 0.00 0.00 5 712 0.59 0.15 0.85 0.00 0.52 0.00 0.00 0.48 14 713 0.00 0.00 1.00 0.00 1.00 0.00 0.00 0.00 3 714 0.40 0.20 0.53 0.00 0.00 0.00 0.00 0.00 5 715 0.33 0.18 0.10 0.00 0.23 0.00 0.00 0.77 20 716 0.67 0.16 0.23 0.00 0.00 0.00 0.00 0.34 7 717 0.00 0.44 0.10 0.00 0.42 0.00 0.00 0.58 5 718 0.00 1.00 0.00 0.00 0.48 0.42 0.00 0.00 3 719 0.00 0.00 0.00 1.00 0.00 0.00 0.00 1.00 3 720 0.00 0.79 0.21 0.00 0.00 0.00 0.00 0.00 4 721 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0.00 3 722 0.00 0.00 1.00 0.00 0.00 1.00 0.00 0.00 0 723 0.00 0.23 0.77 0.00 0.00 0.00 0.00 1.00 4 724 0.03 0.35 0.65 0.00 0.87 0.00 0.00 0.13 3 725 0.83 0.72 0.25 0.00 0.98 0.02 0.00 0.00 16 726 0.00 0.00 0.00 1.00 0.00 0.00 0.00 1.00 4 727 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0.00 0 728 0.32 0.31 0.48 0.00 1.00 0.00 0.00 0.00 12 729 0.00 0.25 0.74 0.00 0.74 0.26 0.00 0.00 6 730 0.00 0.00 0.00 0.63 0.00 0.00 0.08 0.00 1 731 0.02 0.00 1.00 0.00 0.00 1.00 0.00 0.00 4 732 0.00 0.00 0.00 1.00 0.00 0.00 0.00 1.00 3 733 0.96 0.15 0.00 0.00 0.45 0.00 0.00 0.55 7 734 0.00 0.00 0.00 1.00 0.00 0.35 0.00 0.00 0 735 0.00 0.00 0.00 0.47 1.00 0.00 0.00 0.00 36 736 0.00 0.00 1.00 0.00 1.00 0.00 0.00 0.00 7 737 0.00 0.00 0.00 1.00 0.58 0.21 0.00 0.00 6 738 0.00 0.00 0.00 1.00 0.00 0.00 0.29 0.00 0 739 0.30 0.00 0.00 0.70 0.00 0.00 0.00 1.00 11 740 0.00 0.00 1.00 0.00 0.00 0.25 0.00 0.00 0 741 0.00 0.00 0.00 1.00 0.00 0.00 0.00 1.00 0 742 0.61 0.38 0.15 0.00 0.43 0.57 0.00 0.00 12 743 0.00 0.00 1.00 0.00 0.00 1.00 0.00 0.00 0 744 0.64 0.43 0.03 0.00 0.00 0.22 0.00 0.00 8 745 0.00 0.42 0.57 0.00 0.00 0.00 0.00 0.00 3 746 0.00 0.00 0.00 1.00 0.00 1.00 0.00 0.00 0 747 0.44 0.78 0.04 0.00 0.00 1.00 0.00 0.00 4 748 0.86 0.00 0.00 0.14 0.28 0.00 0.00 0.72 5 749 0.00 0.00 1.00 0.00 0.00 1.00 0.00 0.00 1 750 0.00 0.00 0.00 0.00 0.00 1.00 0.00 0.00 0 751 0.79 0.47 0.00 0.00 1.00 0.00 0.00 0.00 3 752 0.00 0.00 1.00 0.00 0.79 0.21 0.00 0.00 3 753 0.00 0.00 0.00 1.00 0.08 0.00 0.00 0.92 4 754 0.00 0.00 0.00 1.00 0.00 0.72 0.00 0.00 1 755 0.00 0.00 1.00 0.00 0.00 1.00 0.00 0.00 0 756 0.00 0.00 0.00 1.00 1.00 0.00 0.00 0.00 3 757 0.00 0.00 1.00 0.00 0.00 0.00 0.00 1.00 3 758 0.00 0.00 0.00 1.00 0.00 0.75 0.00 0.00 3 759 0.00 1.00 0.00 0.00 0.00 0.00 0.00 1.00 2 760 0.00 1.00 0.00 0.00 0.00 0.00 0.00 1.00 3 761 0.00 0.48 0.52 0.00 0.00 0.00 0.00 1.00 1 762 1.00 0.00 0.00 0.00 0.30 0.00 0.00 0.70 2 763 0.00 0.00 1.00 0.00 0.00 0.00 0.00 1.00 8 764 0.00 1.00 0.16 0.00 0.86 0.14 0.00 0.00 5 765 1.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 4 766 0.00 0.75 0.24 0.00 0.00 0.00 0.00 1.00 0 767 1.00 0.05 0.00 0.00 0.00 0.00 0.00 1.00 4 768 0.07 0.50 0.47 0.00 0.94 0.00 0.00 0.06 14 769 0.00 0.15 0.85 0.00 0.76 0.00 0.00 0.24 1 770 0.00 0.00 1.00 0.00 0.76 0.00 0.00 0.24 6 771 1.00 0.49 0.38 0.00 0.60 0.40 0.00 0.00 16 772 1.00 0.00 1.00 0.00 0.44 0.00 0.00 0.55 4 773 0.00 0.08 0.92 0.00 0.00 0.00 0.00 0.73 5 774 1.00 0.00 0.00 0.00 1.00 0.00 0.00 0.00 6 775 1.00 0.00 0.00 0.00 0.88 0.00 0.12 0.00 2 776 1.00 0.00 0.00 0.00 0.28 0.00 0.00 0.72 5 777 0.00 0.78 0.22 0.00 0.00 0.00 0.00 1.00 5 778 0.71 0.24 1.00 0.00 0.00 1.00 0.00 0.00 4 779 0.70 0.26 0.21 0.00 0.00 1.00 0.00 0.00 10 780 0.00 0.00 1.00 0.00 0.00 1.00 0.00 0.00 3 781 0.00 0.00 0.00 1.00 0.00 0.00 0.00 1.00 1 782 0.00 0.00 0.00 1.00 0.00 0.00 0.00 0.64 6 783 0.00 0.00 1.00 0.00 0.00 0.00 0.11 0.75 4 784 0.00 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0 785 1.00 0.86 0.19 0.00 0.00 1.00 0.00 0.00 5 786 0.00 0.00 0.00 1.00 0.00 0.00 0.00 1.00 5 787 0.00 0.88 0.11 0.00 0.00 0.00 0.00 1.00 6 788 0.15 0.55 0.35 0.00 0.00 0.00 0.00 1.00 8 789 0.45 0.26 0.35 0.00 0.00 0.00 0.00 0.57 11 790 0.00 0.19 0.81 0.00 0.56 0.00 0.00 0.44 5 791 0.00 0.00 1.00 0.00 1.00 0.00 0.00 0.00 3 792 0.00 0.50 0.50 0.00 0.56 0.00 0.00 0.44 4 793 0.09 0.91 0.03 0.00 0.28 0.00 0.00 0.72 10 794 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0.00 0 795 0.00 0.40 0.60 0.00 0.00 0.00 0.00 0.00 1 796 0.00 0.00 0.00 1.00 0.00 0.00 0.00 1.00 3 797 0.00 0.00 1.00 0.00 0.00 1.00 0.00 0.00 0 798 0.00 0.12 0.88 0.00 0.00 0.59 0.00 0.41 0 799 1.00 0.00 0.00 0.00 1.00 0.00 0.00 0.00 2 800 0.00 0.00 1.00 0.00 0.00 1.00 0.00 0.00 3 801 0.00 0.00 0.00 1.00 0.00 0.00 0.00 1.00 7 802 0.00 0.00 0.00 1.00 0.00 1.00 0.00 0.00 0 803 1.00 0.00 1.00 0.00 0.00 1.00 0.00 0.00 2 804 0.00 0.00 0.00 1.00 0.22 0.78 0.00 0.00 13 805 0.00 0.00 0.00 1.00 1.00 0.00 0.00 0.00 1 806 0.52 0.24 0.77 0.00 0.85 0.15 0.00 0.00 16 807 0.00 0.32 0.67 0.00 0.00 0.00 0.00 0.00 0 808 0.00 0.00 0.00 1.00 1.00 0.00 0.00 0.00 5 809 0.00 0.00 0.00 1.00 0.45 0.00 0.00 0.55 6 810 0.37 0.42 0.33 0.00 0.29 0.00 0.00 0.71 2 811 0.28 0.64 0.00 0.00 0.00 0.00 0.00 1.00 14 812 0.00 0.00 0.00 1.00 0.47 0.00 0.00 0.53 1 813 0.00 0.00 0.00 1.00 0.00 1.00 0.00 0.00 1 814 0.00 0.00 0.00 1.00 0.72 0.21 0.00 0.00 5 815 0.00 0.00 1.00 0.00 0.00 0.00 0.00 1.00 1 816 0.00 0.00 0.00 1.00 0.00 0.00 0.00 0.00 4 817 0.97 0.05 0.86 0.00 0.15 0.00 0.00 0.85 14 818 0.14 0.27 0.62 0.00 0.84 0.10 0.00 0.05 15 819 0.00 0.39 0.63 0.00 0.00 0.00 0.00 1.00 7 820 0.15 0.30 0.57 0.00 0.13 0.00 0.00 0.87 20 821 0.00 0.00 0.00 0.00 0.00 1.00 0.00 0.00 0 822 0.32 0.24 0.54 0.00 0.00 0.00 0.00 1.00 2 823 0.00 0.08 0.92 0.00 1.00 0.00 0.00 0.00 3 824 0.00 0.00 1.00 0.00 1.00 0.00 0.00 0.00 2 825 0.00 0.00 0.00 1.00 0.17 0.83 0.00 0.00 6 826 0.00 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0 827 0.00 0.00 0.00 1.00 0.00 1.00 0.00 0.00 0 828 0.00 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0 829 0.00 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0 830 0.00 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0 831 0.00 0.00 0.00 1.00 0.00 0.00 0.00 0.00 1 832 1.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 16 833 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0.00 0 834 0.00 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0 835 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0.00 0 836 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0.00 0 837 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0.00 0 838 0.00 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0 839 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0.00 0 840 0.00 0.00 0.00 1.00 0.00 0.87 0.13 0.00 0 841 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0.00 0 842 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0.00 0 843 0.00 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0 844 0.00 0.00 0.00 1.00 0.00 0.00 0.00 1.00 0 845 0.00 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0 846 0.00 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0 847 0.00 0.04 0.96 0.00 0.00 0.74 0.00 0.00 0 848 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0.00 0 849 0.00 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0 850 0.00 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0 851 0.00 0.68 0.31 0.00 0.00 0.00 0.00 0.00 0 852 0.00 0.00 0.00 1.00 0.00 0.61 0.39 0.00 0 853 0.00 0.73 0.21 0.00 0.88 0.00 0.00 0.00 4 854 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0.00 0 855 0.00 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0 856 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0.00 1 857 0.00 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0 858 0.00 0.38 0.61 0.00 0.00 0.00 0.00 0.00 0 859 0.00 0.00 0.00 1.00 0.00 1.00 0.00 0.00 0 860 0.00 0.00 0.00 1.00 0.00 0.00 0.00 0.00 5 861 0.00 0.00 0.00 1.00 0.72 0.00 0.28 0.00 3 862 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0.00 0 863 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0.00 0 864 0.00 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0 865 0.00 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0

In one example, detecting increased methylation at any one or more of the DMRs set forth in rows 1-822 of Table 1 in a test sample relative to a reference level of methylation for the corresponding one or more DMRs is indicative of the subject having breast cancer. Alternatively, or in addition, detecting decreased methylation at any one or more of the DMRs set forth in rows 823-865 of Table 1 in a test sample relative to a reference level of methylation for the corresponding one or more DMRs is indicative of the subject having breast cancer.

Detecting differential methylation at a single CpG dinucleotide sequence within any one of the genomic regions defined in Table 1 may be indicative of the subject having breast cancer.

Alternatively, detecting differential methylation at two or more CpG dinucleotides within any genomic region defined in Table 1 may be indicative of the subject having breast cancer. For example, detecting differential methylation at two or more CpG dinucleotides, or three or more CpG dinucleotides, or four or more CpG dinucleotides, or five or more CpG dinucleotides, or six or more CpG dinucleotides, or seven or more CpG dinucleotides, or eight or more CpG dinucleotides, or nine or more CpG dinucleotides, or 10 or more CpG dinucleotides, or 20 or more CpG dinucleotides, or 30 or more CpG dinucleotides, or 40 or more CpG dinucleotides, or 50 or more CpG dinucleotides within a genomic region set forth in Table 1 may be indicative of the subject having breast cancer. The two or more CpG dinucleotides may be consecutive (i.e., contiguous) within a genomic region. Alternatively, the two or more CpG dinucleotides may not be consecutive (i.e., may not be contiguous) within any genomic region.

Detecting differential methylation of at least one CpG dinucleotide within two or more different genomic regions set forth in Table 1 may be indicative of the subject having breast cancer. For example, detecting differential methylation at a CpG dinucleotide within two or more, or three or more, or four or more, or five or more, or six or more, or seven or more, or eight or more, or nine or more, or 10 or more different genomic regions set forth in Table 1 may be indicative of the subject having breast cancer.

In one example, detecting differential methylation of at least one CpG dinucleotide residing within a CpG island defined in Table 1 is indicative of the subject having breast cancer. In this regard, Table 1 provides details of genomic regions residing in, or overlapping with, one or more CpG islands. Such genomic regions will be recognised by a person skilled in the art.

In another example, detecting differential methylation of at least one CpG dinucleotide residing within a CpG shore defined in Table 1 is indicative of the subject having breast cancer. In this regard, Table 1 provides details of genomic regions residing in, or overlapping with, a CpG shore. Such genomic regions will be recognised by a person skilled in the art.

In another example, detecting differential methylation of at least one CpG dinucleotide within one or more genomic regions defined in Table 1 associated with, or spanning, a promoter region e.g., such as a CpG island promoter of NPY, FERD3L, HMX2, SATB2 and/or C9orf125, is indicative of the subject having breast cancer.

In another example, detecting differential methylation of at least one CpG dinucleotide within one or more genomic regions defined in Table 1 associated with, or spanning, a transcription factor e.g., such as BARHL2, DLX6, OTX2, RUNX1T1 and/or TAC1, is indicative of the subject having breast cancer.

In another example, detecting differential methylation of at least one CpG dinucleotide within one or more genomic regions defined in Table 1 associated with, or spanning, a signalling pathway gene e.g., such as BADRB3, GHSR, NPY and/or ROBO3, is indicative of the subject having breast cancer.

In another example, detecting differential methylation of at least one CpG dinucleotide within one or more genomic regions defined in Table 1 associated with, or spanning, a promoter region e.g., such as in C9orf125, COL14A1, ENPP2, ERG2, PLD5, ROBO3, RUNX1T1, SEMA5A, TBX18, TSHZ3, ZBTB16, and/or ZNF208, is indicative of the subject having breast cancer. In certain examples, the promoter includes a mutation and is downregulated.

In another example, detecting differential methylation of at least one CpG dinucleotide within one or more genomic regions defined in Table 1 associated with, or spanning, a gene involved in the axon guidance pathway e.g., such as CRMP1, GDNF, GFRA1, MYL9, ROBO1, ROBO3 and/or SEMA5A, is indicative of the subject having breast cancer. For example, detecting hypermethylation of at least one CpG dinucleotide within one or more genomic regions defined in Table 1 associated with, or spanning, a gene involved in the axon guidance pathway e.g., such as CRMP1, GDNF, GFRA1, MYL9, ROBO1, ROBO3 and/or SEMA5A, is indicative of the subject having breast cancer.

It will be understood that the methods described herein encompass determining methylation status of any combination of CpG dinucleotide sequences in any combination of genomic regions set forth in Table 1, in any permutation. For example, the methods disclosed herein may comprise determining the methylation status of any one or more CpG dinucleotide sequences in any 2 or more, 3 or more, 4 or more, 5 or more, 10 or more, 20 or more, 30 or more, 40 or more, 50 or more, 60 or more, 70 or more, 80 or more, 90 or more, or 100 or more genomic regions set forth in Table 1, in any permutation. In this regard, the inventors have shown e.g., in Examples 5 and 6 herein, that small subsets of probes configured to detect differential methylation at CpG dinucleotide sequences within genomic regions set forth in Table 1 e.g., 2 or 3 or 4 or 5 probes, are capable of discriminating TNBC from non-TNBC with high sensitivity and specificity.

For example, a subset of probes configured to detect differential methylation at CpG dinucleotide sequences within the 2 or more, 3 or more, 4 or more, 5 or more, 10 or more, 20 or more, 30 or more, 40 or more, 50 or more, 60 or more, 70 or more, 80 or more, 90 or more, or 100 or more genomic regions set forth in Table 1 may include 1 or more, or 2 or more, or 3 or more, or 4 or more, or 5 or more, or 6 or more, or 7 or more or all probes selected from:

-   -   cg0804822—promoter of ZNF671;     -   cg09368188—intronic region of KIF26B;     -   cg00421363—promoter region of PPFIA3;     -   cg04781584—promoter region of PPFIA3;     -   cg03559454—promoter region of PPFIA3;     -   cg01926238—distant promoter of VGLL2;     -   cg05650260—intronic region of TFAP2D; and     -   cg04416734—exonic region of ALDOA.

Alternatively, or in addition, probes configured to detect differential methylation at CpG dinucleotide sequences within the 2 or more, 3 or more, 4 or more, 5 or more, 10 or more, 20 or more, 30 or more, 40 or more, 50 or more, 60 or more, 70 or more, 80 or more, 90 or more, or 100 or more genomic regions set forth in Table 1 may include 1 or more, or 2 or more, or 3 or more, or 4 or more, or 5 or more, or 6 or more, or 7 or more or all probes selected from:

-   -   cg11977686—promoter of ZNF671     -   cg13484549—promoter of PPFIA3     -   cg08048222—promoter of ZNF671     -   cg00421363—promoter of PPFIA3     -   cg01926238—distant promoter of VGLL2     -   cg08398233—intronic region of CAMK2N1     -   cg07802350—distant promoter of HOXD13     -   cg20095233—distant promoter of GFRA1     -   cg13473196—intronic region of CKAP5

Generally, the greater the number of CpG dinucleotides assessed for methylation status, the more reliable the diagnosis and/or prognosis of the subject. Thus, the greater the number of genomic regions defined in Table 1 for which methylation status is determined in the methods disclosed herein, the more reliable the diagnosis or prognosis of the subject.

Particular individual CpG dinucleotides within any of the genomic regions identified in Table 1 may be particularly strong predictors of the presence of breast cancer, or of the presence of a particular subtype of breast cancer e.g., such as ER negative breast cancer or TNBC. For example, detecting differential methylation of a CpG dinucleotide sequence within the Wilms tumour protein (WT1) gene and/or its antisense counterpart, WT1-AS, e.g., such as within one or more of the DMRs designated chr11-1163, chr11-1210 and/or chr11-4047 (see, e.g., FIG. 3F), is predictive of the presence of breast cancer, including the presence of a TNBC.

Differential methylation of one or more CpG dinucleotides in any one of more of the genomic regions defined in Table 1 may alter expression of a gene within which a CpG resides. Accordingly, expression levels of genes associated with any of the genomic regions defined in Table 1 may be used as a predictor of the presence of breast cancer, or of the presence of a particular subtype of breast cancer e.g., such as ER negative breast cancer or TNBC.

The present disclosure also provides methods of diagnosing ER−ve breast cancer specifically, involving determining the methylation status of one or more CpG dinucleotide sequences within one or more genomic regions set forth in Table 1 in a test sample obtained from a subject, and identifying differential methylation at said one or more CpG dinucleotide sequences within one or more genomic regions in the test sample relative to a reference level of methylation for the corresponding one or more CpG dinucleotide sequences.

The present disclosure also provides methods of diagnosing triple negative breast cancer (TNBC) specifically, involving determining the methylation status of one or more CpG dinucleotides within one or more genomic regions set forth in Table 2 and/or Table 3 in a test sample obtained from a subject, and determining differential methylation of said one or more CpG dinucleotides at said one or more genomic regions in the test sample relative to a reference level of methylation for the corresponding one or more CpG dinucleotides at said one or more genomic regions.

Differential methylation of said one or more CpG dinucleotides within one or more of the genomics regions set forth in Table 2 relative to the reference level is indicative of the subject having TNBC. In this regard, Table 2 provides a list of 36 regions identified to be more methylated in tumours of TNBC breast cancer subtype as compared to tumours in other breast cancer subtypes. The genomic regions set forth in Table 2 are defined with reference to human genome assembly version 18 (hg18). For each DMR, the following information is provided in Table 2:

-   (i) unique DMR identifier (Column 1); -   (ii) genomic coordinates of DMR with respect to hg18 (Columns 2-4); -   (iii) transcript IDs and gene names for regions overlapping RefSeq     promoter and transcript body regions (Columns 5-6); -   (iv) whether the corresponding gene is up-/down-regulated in TNBC     (Column 7); -   (v) expression percentile of the corresponding gene in normal     samples (Column 8); -   (vi) overlap (fraction of the region overlapping the element) with     various functional elements of the human genome (Columns 9-15); -   (vii) number of overlapping TNBC specific HM450K probes, probes     showing statistically significant hypermethylation in TNBC tumours     as compared to tumours of other breast cancer subtypes (Column 16); -   (viii) number of HM450K probes which are TNBC specific (Column 17);     and -   (ix) number of HM450K probes with statistically significant     association with poor/good prognosis in subjects with TNBC (Columns     18-19).

In one example, detecting differential methylation of at least one CpG dinucleotide residing within a CpG island defined in Table 2 is indicative of the subject having TNBC specifically, as opposed to other breast cancer subtypes. For example, Table 2 provides details of genomic regions residing in, or overlapping with, zinc finger proteins e.g., such as ZNF154 and/or ZNF671. Such genomic regions may be silenced as a result of methylation of the one or more CpG dinucleotides.

The present disclosure also provides methods of prognosis of, predicting the therapeutic outcome of, and/or monitoring progression of, triple negative breast cancer (TNBC) in a subject, comprising detecting the methylation status of one or more CpG dinucleotides within one or more genomic regions set forth in Table 3 in a test sample obtained from the subject, and determining differential methylation at said one or more genomic regions in the test sample relative to a reference level of methylation for the corresponding one or more CpG dinucleotides, wherein differential methylation at said one or more CpG dinucleotides within the genomic regions set forth in Table 3 relative to the reference level of methylation is correlated with a prognosis and/or a prediction of the therapeutic outcome of the TNBC.

For example, the prognosis performed in the methods disclosed herein may comprise determining a disease outcome in a subject suffering from TNBC. In this regard, Table 3 provides a list of 17 DMRs identified as being associated with disease outcome, such as an increased or decreased likelihood of survival, in TNBC. The genomic regions set forth in Table 3 are defined with reference to human genome assembly version 18 (hg18). For each DMR, the following information is provided in Table 3:

-   (i) DMR number and unique identifier (Columns 1-2); -   (ii) genomic coordinates of DMR with respect to hg18 (Columns 3-5); -   (iii) transcript IDs and gene names for regions overlapping RefSeq     promoter and transcript body regions (Columns 6-7); -   (iv) overlap (fraction of the region overlapping the element) with     various functional elements of the human genome (Columns 8-17); -   (v) number of overlapping TNBC specific HM450K probes, probes     showing statistically significant hypermethylation in TNBC tumours     as compared to tumours of other breast cancer subtypes (Column 18);     and -   (vi) number of HM450K probes with statistically significant     association with poor/good prognosis in subjects with TNBC (Columns     19-20).

In one example, detecting differential methylation of at least one CpG dinucleotide within one or more genomic regions defined in Table 3 associated with or spanning a promoter e.g., such as a promoter of SLC6A3, C6orf174, WT1-AS and/or ZNF254, and/or associated with or spanning a gene body e.g., DMRTA2, LHX8, WT1, WT1-AS, HOXB13, ECEL1 and/or SOX2-OT, and/or associated with or spanning an intergenic region, is associated disease outcome, such as an increased or decreased likelihood of survival, in TNBC.

Table 14 provides an alternative list of 20 DMRs identified as being associated with disease outcome, such as an increased or decreased likelihood of survival, in TNBC. The genomic regions set forth in Table 14 are defined with reference to human genome assembly version 18 (hg18). For each DMR, the following information is provided in Table 3:

-   (i) chromosome (Column 1); -   (ii) genomic coordinates of DMR with respect to hg18 (Columns 2-3); -   (iii) region name (Column 4); and -   (iv) associated Figure.

Accordingly, any of the methods disclosed herein may comprise detecting methylation status at one or more CpG dinucleotides within one or more regions set forth in Table 14.

TABLE 2 Differentially methylated regions (DMRs) associated with Triple Negative Breast Cancer (TN-BC). Agilent Agilent DMR TNBC TNBC unique RefSeq RefSeq Normal Normal RefSeq: RefSeq: identifier Chromosome start end Promoter Body vs Tumor Percentile % exon % intron chr1-19649 1 20,682,759 20,684,507 CAMK2N1 No change 75-100 0.15 0.85 chr1-4856 1 103,346,188 103,347,175 COL11A1 COL11A1 Up 50-75 0.43 0.03 chr1-38532 1 246,086,842 246,087,566 TRIM58 TRIM58 No change  0-25 0.61 0.00 chr11-25406 11 31,775,677 31,779,112 PAX6 No change  0-25 0.05 0.95 chr11-18108 11 31,802,820 31,804,364 RCN1 No change 50-75 0.00 1.00 chr11-1210 11 32,416,010 32,417,947 WT1-AS NA NA 1.00 0.00 chr13-14599 13 111,756,947 111,759,018 0.00 0.00 chr13-12246 13 111,759,028 111,761,130 0.00 0.00 chr13-16458 13 111,764,441 111,766,059 0.00 0.00 chr13-16371 13 111,806,052 111,808,603 0.00 0.00 chr16-43017 16 6,009,572 6,010,985 RBFOX1 NA NA 0.30 0.70 chr18-245 18 53,255,803 53,260,282 ONECUT2 Up  0-25 0.00 1.00 chr19-44228 19 3,385,736 3,386,568 NFIC No change 50-75 0.15 0.85 chr19-36061 19 12,166,237 12,167,686 0.00 0.00 chr19-14446 19 22,260,848 22,261,568 ZNF729 ZNF729 NA NA 0.15 0.51 chr19-36571 19 24,061,637 24,062,272 ZNF254 ZNF254 No change 50-75 0.26 0.46 chr19-46114 19 54,337,512 54,338,223 PPFIA3 Up  0-25 0.28 0.72 chr19-31544 19 62,911,410 62,912,734 ZNF154 ZNF154 Down 50-75 0.21 0.54 chr19-6109 19 62,930,239 62,930,957 ZNF671 ZNF671 Down 75-100 0.33 0.46 chr21-8209 21 36,989,873 36,993,170 SIM2 up  0-25 0.00 0.00 chr3-18012 3 27,740,141 27,741,506 EOMES No change 75-100 0.00 0.00 chr3-1810 3 132,563,160 132,564,028 NR_002949 NR_002949 NA NA 0.69 1.00 NR_027766 NR_027766 NR_038976 chr3-22112 3 148,624,151 148,625,148 0.00 0.00 chr3-1158 3 173,648,106 173,650,702 GHSR GHSR No change 50-75 0.32 0.00 chr6-3486 6 26,853,218 26,853,781 0.00 0.00 chr6-7514 6 26,865,279 26,866,569 0.00 0.00 chr6-25261 6 27,755,606 27,757,375 0.00 0.00 chr6-15498 6 50,799,906 50,800,777 TFAP2D NA NA 0.00 1.00 chr6-1806 6 53,256,334 58,257,651 0.00 0.00 chr6-28662 6 117,691,091 117,691,644 VGLL2 Down 50-75 0.00 0.00 chr7-23113 7 24,290,275 24,291,795 NPY NPY No change  0-25 0.18 0.78 chr8-19800 8 65,443,925 65,445,545 0.00 0.00 chr8-15516 8 65,446,926 65,447,225 NR_034102 NR_034102 NA NA 0.00 0.00 chr8-8662 8 65,449,063 65,449,743 NR_034102 NA NA 0.00 1.00 chr8-3384 8 65,454,075 65,455,349 MIR124-2 MIR124-2 NA NA 0.08 0.00 chr8-17320 8 145,395,909 145,896,990 0.00 0.00 DMR RefSeq: ChromHMM ChromHMM ChromHMM ChromHMM HM450K: HM450K: HM450K: HM450K: unique % HMEC: HMEC: HMEC: HMEC: number TNBC Bad Good identifier intergenic % promoter % enhancer % insulator % polycomb of probes Specific Prognosis Prognosis chr1-19649 0.00 1.00 0.00 0.00 0.00 8 3 0 0 chr1-4856 0.00 0.00 0.22 0.00 0.00 8 3 0 0 chr1-38532 0.00 0.00 0.00 0.00 0.00 9 6 0 0 chr11-25406 0.00 0.23 0.00 0.00 0.77 22 5 0 0 chr11-18108 0.00 0.00 0.00 0.00 1.00 8 3 2 0 chr11-1210 0.00 0.00 0.00 0.00 1.00 10 3 4 0 chr13-14599 1.00 0.00 0.00 0.00 1.00 8 3 0 0 chr13-12246 1.00 0.00 0.00 0.00 1.00 9 4 0 0 chr13-16458 1.00 0.00 0.00 0.00 1.00 7 4 0 0 chr13-16371 1.00 0.00 0.00 0.00 0.71 10 3 0 0 chr16-43017 0.00 0.00 0.00 0.00 0.00 5 3 0 0 chr18-245 0.00 0.45 0.00 0.00 0.55 15 5 0 0 chr19-44228 0.00 0.20 0.80 0.00 0.00 4 4 0 0 chr19-36061 1.00 0.89 0.00 0.00 0.00 7 5 0 0 chr19-14446 0.00 0.00 0.00 0.00 0.00 3 3 0 0 chr19-36571 0.00 1.00 0.00 0.00 0.00 5 3 0 4 chr19-46114 0.00 0.59 0.41 0.00 0.00 5 5 0 0 chr19-31544 0.00 1.00 0.00 0.00 0.00 11 8 0 0 chr19-6109 0.00 1.00 0.00 0.00 0.00 7 7 0 0 chr21-8209 0.60 0.11 0.00 0.00 0.89 20 3 0 0 chr3-18012 0.52 0.59 0.04 0.00 0.37 11 5 1 0 chr3-1810 0.00 0.97 0.00 0.00 0.00 9 6 0 0 chr3-22112 1.00 0.00 0.00 0.00 1.00 8 5 2 0 chr3-1158 0.00 0.35 0.00 0.00 0.65 16 3 2 0 chr6-3486 1.00 0.68 0.00 0.00 0.32 3 3 0 0 chr6-7514 1.00 1.00 0.00 0.00 0.00 6 3 0 0 chr6-25261 1.00 0.22 0.78 0.00 0.00 13 4 2 0 chr6-15498 0.00 0.46 0.00 0.00 0.54 6 3 1 0 chr6-1806 1.00 0.96 0.00 0.00 0.04 6 5 0 0 chr6-28662 0.58 0.00 0.00 0.00 1.00 4 3 1 0 chr7-23113 0.00 0.48 0.00 0.00 0.52 10 3 2 0 chr8-19800 1.00 0.00 0.00 0.00 1.00 9 3 0 0 chr8-15516 0.00 0.00 0.00 0.00 1.00 4 4 0 0 chr8-8662 0.00 0.00 0.00 0.00 1.00 5 5 0 0 chr8-3384 0.00 0.00 0.00 0.00 1.00 11 11 0 0 chr8-17320 1.00 0.37 0.18 0.00 0.00 8 3 0 0

TABLE 3 Differentially methylated regions (DMRs) associated with overall survival in Triple Negative Breast Cancer (TNBC) CpG DMR RefSeq Islands: CpG Row unique RefSeq Gene % Islands: No. identifier Chromosome start end Promoter Body Island % shore 1 chr1-17872 1 50,658,646 50,659,783 DMRTA2 1.00 0.00 2 chr1-47207 1 75,368,128 75,368,976 LHX8 0.41 0.59 3 chr10-13741 10 102,409,068 102,409,766 0.75 0.25 4 chr11-11623 11 32,404,535 32,407,465 WT1 0.51 0.49 5 chr11-1210 11 32,416,010 32,417,947 WT1-AS 0.45 0.55 6 chr13-5199 13 27,398,788 27,401,867 0.30 0.70 7 chr14-13134 14 56,330,541 56,332,135 0.78 0.22 8 chr17-22033 17 44,159,065 44,159,578 HOXB13 0.41 0.59 9 chr2-4402 2 233,058,433 233,060,592 ECEL1 0.96 0.04 10 chr3-13593 3 182,923,564 182,924,686 SOX2-OT 0.00 0.00 11 chr5-29013 5 1,498,811 1,499,696 SLC6A3 0.95 0.05 12 chr6-21729 6 27,620,848 27,621,582 0.00 0.00 13 chr6-24682 6 127,881,341 127,882,455 C6orf174 C6orf174 0.00 0.05 14 chr7-22735 7 121,726,837 121,728,266 0.45 0.55 15 chr11-4047 chr11 32,413,697 32,415,714 WT1 WT1-AS 0.09 0.91 WT1-AS 16 chr19-36571 chr19 24,061,637 24,062,272 ZNF254 ZNF254 0.00 0.00 17 chr22-16101 chr22 44,641,414 44,642,542 0.88 0.12 RefSeq: RefSeq: ChromHMM ChromHMM ChromHMM ChromHMM HM450K: HM450K: HM450K: Row % RefSeq: RefSeq: % HMEC: HMEC: HMEC: HMEC: Number Poor Good No. promoter % exon % intron intergenic % promoter % enhancer % insulator % polycomb of Probes Prognosis Prognosis 1 0.00 0.48 0.52 0.00 0.34 0.00 0.00 0.66 7 3 2 0.00 0.56 0.44 0.00 0.00 0.00 0.00 1.00 4 3 3 0.00 0.00 0.00 1.00 0.00 0.00 0.00 1.00 6 5 4 0.00 0.08 0.92 0.00 0.00 0.00 0.00 1.00 15 5 5 0.00 1.00 0.00 0.00 0.00 0.00 0.00 1.00 10 4 6 0.00 0.00 0.00 0.46 0.00 0.00 0.00 1.00 14 4 7 0.00 0.00 0.00 1.00 0.00 0.00 0.00 1.00 6 3 8 0.00 0.66 0.34 0.00 0.00 0.00 0.00 1.00 4 3 9 0.00 0.41 0.59 0.00 0.37 0.00 0.00 0.63 10 3 10 0.00 0.00 1.00 0.00 0.00 0.00 0.00 1.00 8 4 11 1.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 4 3 12 0.00 0.00 0.00 1.00 0.75 0.00 0.00 0.00 8 3 13 0.32 0.70 0.07 0.00 0.18 0.00 0.00 0.82 11 6 14 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 8 4 15 0.97 0.05 0.86 0.00 0.15 0.00 0.00 0.85 14 5 16 0.44 0.26 0.46 0.00 1.00 0.00 0.00 0.00 5 4 17 0.00 0.00 0.00 1.00 0.87 0.00 0.00 0.13 7 3

Detecting differential methylation at a single CpG dinucleotide sequence within any one of the genomic regions defined in Table 3 may be predictive of an increased or a decreased likelihood of survival of the subject.

Similarly, detecting differential methylation at a single CpG dinucleotide sequence within any one of the genomic regions defined in Table 14 may be predictive of an increased or a decreased likelihood of survival of the subject. Examples of CpG dinucleotide sequences within the genomic regions defined in Table 14 which may be predictive of an increased or a decreased likelihood of survival of the subject are provided in Table 15. Thus, a method of predicting increased or a decreased likelihood of survival of a subject may comprise detecting methylation status of a single CpG dinucleotide sequence set forth in Table 15.

Alternatively, detecting differential methylation at any two or more CpG dinucleotides within any genomic region set forth in Table 3 may be predictive of an increased or a decreased likelihood of survival of the subject. For example, detecting differential methylation at two or more CpG dinucleotides, or three or more CpG dinucleotides, or four or more CpG dinucleotides, or five or more CpG dinucleotides, or six or more CpG dinucleotides, or seven or more CpG dinucleotides, or eight or more CpG dinucleotides, or nine or more CpG dinucleotides, or 10 or more CpG dinucleotides with a genomic region set forth in Table 3 may be predictive of an increased or a decreased likelihood of survival of the subject.

Similarly, detecting differential methylation at two or more CpG dinucleotide sequence within any one of the genomic regions defined in Table 14 may be predictive of an increased or a decreased likelihood of survival of the subject. For example, detecting differential methylation at two or more CpG dinucleotides, or three or more CpG dinucleotides, or four or more CpG dinucleotides, or five or more CpG dinucleotides, or six or more CpG dinucleotides, or seven or more CpG dinucleotides, or eight or more CpG dinucleotides, or nine or more CpG dinucleotides, or 10 or more CpG dinucleotides with a genomic region set forth in Table 14 may be predictive of an increased or a decreased likelihood of survival of the subject. Thus, a method of predicting increased or a decreased likelihood of survival of a subject may comprise detecting methylation status of two or more CpG dinucleotide sequences set forth in Table 15.

Alternatively or in addition, detecting differential methylation of at least one CpG dinucleotide within two or more different genomic regions set forth in Table 3 can be predictive of an increased or a decreased likelihood of survival of the subject. For example, detecting differential methylation at a CpG dinucleotide within two or more, or three or more, or four or more, or five or more, or six or more, or seven or more, or eight or more, or nine or more, or 10 or more different genomic regions set forth in Table 3 is predictive of an increased or a decreased likelihood of survival of the subject.

Alternatively or in addition, detecting differential methylation of at least one CpG dinucleotide within two or more different genomic regions set forth in Table 14 can be predictive of an increased or a decreased likelihood of survival of the subject. For example, detecting differential methylation at a CpG dinucleotide within two or more, or three or more, or four or more, or five or more, or six or more, or seven or more, or eight or more, or nine or more, or 10 or more different genomic regions set forth in Table 14 is predictive of an increased or a decreased likelihood of survival of the subject.

Generally, the greater the number of CpG dinucleotides assessed for methylation status, the more reliable the prognosis of the subject. Thus, the greater the number of genomic regions defined in Table 3 for which methylation status is determined in the methods disclosed herein, the more reliable the prognosis of the subject. Similarly, the greater the number of genomic regions defined in Table 14 for which methylation status is determined in the methods disclosed herein, the more reliable the prognosis of the subject.

Differential methylation of one or more CpG dinucleotides in any one of more of the genomics regions defined in Table 3 may alter expression of a gene within which a CpG resides. Accordingly, expression levels of genes associated with any of the genomic regions defined in Table 3 may be used to predict of an increased or a decreased likelihood of survival of the subject.

Similarly, differential methylation of one or more CpG dinucleotides in any one of more of the genomics regions defined in Table 14 may alter expression of a gene within which a CpG resides. Accordingly, expression levels of genes associated with any of the genomic regions defined in Table 14 may be used to predict of an increased or a decreased likelihood of survival of the subject.

In one example, detecting increased methylation e.g., such as hypermethylation, at one or more of the DMRs set forth in rows 1-14 of Table 3 for a test sample relative to a reference level of methylation for the corresponding one or more DMRs is predictive that the subject will have a decreased likelihood of survival relative to a subject in which the corresponding DMR(s) do not show increased methylation.

In another example, detecting decreased methylation e.g., such as hypomethylation, at one or more of the DMRs set forth in rows 15-17 of Table 3 for a test sample relative to a reference level of methylation for the corresponding one or more DMRs is predictive that the subject will have an increased likelihood of survival relative to a subject in which the corresponding DMR(s) do not show decreased methylation.

In a particularly preferred example, the method of prognosis disclosed herein comprises detecting differential methylation of a CpG dinucleotide sequence within the Wilms tumour protein (WT1) gene and/or its antisense counterpart, WT1-AS, For example, detecting increased methylation of a CpG dinucleotide sequence within one or more of the DMRs designated chr11-1163 and/or chr11-1210 relative to the reference levels of methylation for those genomic regions is predictive of a decreased likelihood of survival. Alternatively, detecting decreased methylation of a CpG dinucleotide sequence within the DMRs designated chr11-4047 relative to the reference level of methylation for that genomic region is predictive of an increased likelihood of survival.

Breast Cancer Subtypes

The present disclosure provides the diagnosis, prognosis, or prediction of therapeutic outcome of any breast cancer, or cancer caused by a malignant cell derived from a breast. Exemplary breast cancers include basal breast cancer, Her2 positive breast cancer, progesterone receptor positive breast cancer, estrogen receptor positive breast cancer, ductal carcinoma in situ, lobular carcinoma in situ, early breast cancer, invasive breast cancer, Paget's disease of the nipple, inflammatory breast cancer, locally advanced breast cancer and secondary breast cancer. Breast cancer may also be characterised according to various molecular subtypes which are typically categorized on an immunohistochemical basis. Exemplary molecular subtypes of breast cancer are as follows:

-   -   (i) normal (ER+, PR+, HER2+, cytokeratin 5/6+, and HER1+);     -   (ii) luminal A (ER+ and/or PR+, HER2−);     -   (iii) luminal B (ER+ and/or PR+, HER2+);     -   (iv) triple-negative (ER−, PR−, HER2−);     -   (v) HER2+/ER− (ER−, PR−, and HER2+); and     -   (vi) unclassified (ER−, PR−, HER2−, cytokeratin 5/6−, and         HER1−).

Particular combinations of the DMRs identified herein may be particularly useful in the identification of any one or more of these known subtypes of breast cancer.

Diagnostic and/or Prognostic Assay Formats

1. Detection of Methylation of Nucleic Acid and Methods Therefor

The present inventors have identified differentially methylated regions (DMRs) in breast cancer cells compared to non-cancerous cells. The present inventors have also identified specific DMRs in breast cancer cells characterised as being ER−ve or TNBC compared to other breast cancer cells. Furthermore, the present inventors have demonstrated that a subset of DMRs identified in TNBC cells are capable of stratifying TNBC subtypes associated with distinct prognostic profiles e.g., populations of TNBC patients with high, medium or low risk disease outcomes. Accordingly, a method for detecting DMRs as described herein shall be taken to include detecting methylation status of CpG dinucleotide sequences in one or more genomic regions i.e., to determine whether or not a genomic region is differentially methylated relative to a reference level of methylation for the genomic region. Suitable methods for the detection of methylation status are known in the art and/or described herein.

The term “methylation” shall be taken to mean the addition of a methyl group by the action of a DNA methyl transferase enzyme to a CpG island of nucleic acid, e.g., genomic DNA. As described herein, there are several methods known to those skilled in the art for determining the level or degree of methylation of nucleic acid. By “differential methylation” of a nucleic acid it is meant that there is a deviation in the number of methylated CpG dinucleotides at a genomic region within the subject diagnosed compared to that detected within a corresponding genomic region in a suitable control sample i.e., which provides a reference level of methylation for that genomic region. The differentially methylated nucleic acid may have an increased level of methylation within a specific or defined region of nucleic acid e.g., such as hypermethylation, or a decreased level of methylation within a specific or defined region of nucleic acid e.g., such as hypomethylation.

The term “hypermethylation” shall be taken to mean that a plurality of CpG dinucleotides in a specific or defined region of nucleic acid are methylated relative to a reference level.

The term “hypomethylation” shall be taken to mean that a plurality of CpG dinucleotides in a specific or defined region of nucleic acid are unmethylated relative to a reference level.

The present disclosure is not to be limited by a precise number of methylated residues that are considered to be diagnostic of cancer in a subject or predictive of its outcome, because some variation between patient samples will occur. The present disclosure is also not limited by the specific positioning of the methylated residue within a DMR

In one example, the degree of methylation in a subject is determined for one or more genomic regions set forth in Tables 1-3. In one example, the degree of methylation in a subject is determined for one or more genomic regions set forth in Table 1. In one example, the degree of methylation in a subject is determined for one or more genomic regions set forth in Table 2. In one example, the degree of methylation in a subject is determined for one or more genomic regions set forth in Table 3.

a) Probe or Primer Design and/or Production

Several methods described herein for the diagnosis and/or prognosis of breast cancer e.g., such as ER−ve breast cancer or TNBC, use one or more probes and/or primers to detect methylation at a genomic region. Methods for designing probes and/or primers for use in, for example, PCR or hybridization are known in the art and described, for example, in Dieffenbach and Dveksler (Eds) (In: PCR Primer: A Laboratory Manual, Cold Spring Harbor Laboratories, NY, 1995). Furthermore, several software packages are publicly available that design optimal probes and/or primers for a variety of assays, e.g. Primer 3 available from the Center for Genome Research, Cambridge, Mass., USA.

The potential use of the probe or primer should be considered during its design. For example, should the probe or primer be produced for use in, for example, a methylation specific PCR or ligase chain reaction (LCR) assay the nucleotide at the 3′ end (or 5′ end in the case of LCR) should correspond to a methylated nucleotide in a nucleic acid.

Probes and/or primers useful for detection of a marker associated with a cancer are assessed, for example, to determine those that do not form hairpins, self-prime or form primer dimers (e.g. with another probe or primer used in a detection assay).

Methods for producing/synthesizing a probe or primer of the present disclosure are known in the art. For example, oligonucleotide synthesis is described, in Gait (Ed) (In: Oligonucleotide Synthesis: A Practical Approach, IRL Press, Oxford, 1984). For example, a probe or primer may be obtained by biological synthesis (e.g. by digestion of a nucleic acid with a restriction endonuclease) or by chemical synthesis. For short sequences (up to about 100 nucleotides) chemical synthesis is preferable.

Other methods for oligonucleotide synthesis include, for example, phosphotriester and phosphodiester methods (Narang, et al. Meth. Enzymol 68: 90, 1979) and synthesis on a support (Beaucage, et al Tetrahedron Letters 22: 1859-1862, 1981) as well as phosphoramidate technique, Caruthers, M. H., et al., “Methods in Enzymology,” Vol. 154, pp. 287-314 (1988), and others described in “Synthesis and Applications of DNA and RNA,” S. A. Narang, editor, Academic Press, New York, 1987, and the references cited therein.

Probes comprising locked nucleic acid (LNA) are synthesized as described, for example, in Nielsen et al, J. Chem. Soc. Perkin Trans., 1: 3423, 1997; Singh and Wengel, Chem. Commun. 1247, 1998. While, probes comprising peptide-nucleic acid (PNA) are synthesized as described, for example, in Egholm et al., Am. Chem. Soc., 114: 1895, 1992; Egholm et al., Nature, 365: 566, 1993; and Orum et al., Nucl. Acids Res., 21: 5332, 1993.

b) Methylation-Sensitive Endonuclease Digestion of DNA

In one example, the methylation status of one or more genomic regions in a sample is determined using a process comprising treating the nucleic acid with an amount of a methylation-sensitive restriction endonuclease enzyme under conditions sufficient for nucleic acid to be digested and then detecting the fragments produced. Exemplary methylation-sensitive endonucleases include, for example, HpaI or HpaII.

In one example, the digestion of nucleic acid is detected by selective hybridization of a probe or primer to the undigested nucleic acid. Alternatively, the probe selectively hybridizes to both digested and undigested nucleic acid but facilitates differentiation between both forms, e.g., by electrophoresis. Suitable detection methods for achieving selective hybridization to a hybridization probe include, for example, Southern or other nucleic acid hybridization (Kawai et al., Mol. Cell. Biol. 14, 7421-7427, 1994; Gonzalgo et al., Cancer Res. 57, 594-599, 1997).

The term “selectively hybridizable” means that the probe is used under conditions where a target nucleic acid hybridizes to the probe to produce a signal that is significantly above background (i.e., a high signal-to-noise ratio). The intensity of hybridization is measured, for example, by radiolabeling the probe, e.g. by incorporating [α-³⁵S] and/or [α-³²P]dNTPs, [γ-³²P]ATP, biotin, a dye ligand (e.g., FAM or TAMRA), a fluorophore, or other suitable ligand into the probe prior to use and then detecting the ligand following hybridization.

The skilled artisan will be aware that optimum hybridization reaction conditions should be determined empirically for each probe, although some generalities can be applied. Preferably, hybridizations employing short oligonucleotide probes are performed at low to medium stringency.

For the purposes of defining the level of stringency to be used in these diagnostic assays, a low stringency is defined herein as being a hybridization and/or a wash carried out in about 6×SSC buffer and/or about 0.1% (w/v) SDS at about 28° C. to about 40° C., or equivalent conditions. A moderate stringency is defined herein as being a hybridization and/or washing carried out in about 2×SSC buffer and/or about 0.1% (w/v) SDS at a temperature in the range of about 45° C. to about 65° C., or equivalent conditions.

In the case of a GC rich probe or primer or a longer probe or primer a high stringency hybridization and/or wash is preferred. A high stringency is defined herein as being a hybridization and/or wash carried out in about 0.1×SSC buffer and/or about 0.1% (w/v) SDS, or lower salt concentration, and/or at a temperature of at least 65° C., or equivalent conditions. Reference herein to a particular level of stringency encompasses equivalent conditions using wash/hybridization solutions other than SSC known to those skilled in the art.

Generally, the stringency is increased by reducing the concentration of SSC buffer, and/or increasing the concentration of SDS and/or increasing the temperature of the hybridization and/or wash. Those skilled in the art will be aware that the conditions for hybridization and/or wash may vary depending upon the nature of the hybridization matrix used to support the sample DNA, and/or the type of hybridization probe used and/or constituents of any buffer used in a hybridization. For example, formamide reduces the melting temperature of a probe or primer in a hybridization or an amplification reaction.

Conditions for specifically hybridizing nucleic acid, and conditions for washing to remove non-specific hybridizing nucleic acid, are understood by those skilled in the art. For the purposes of further clarification only, reference to the parameters affecting hybridization between nucleic acid molecules is found in Ausubel et al. (Current Protocols in Molecular Biology, Wiley Interscience, ISBN 047150338, 1992), which is herein incorporated by reference.

In accordance with the present example, a difference in the fragments produced for the test sample and a control sample is indicative of the subject having breast cancer. Similarly, in cases where the control sample comprises data from a tumor, cancer tissue, a cancerous cell or pre-cancerous cell e.g., such as from a subject having breast cancer, similarity, albeit not necessarily absolute identity, between the test sample and the control sample is indicative of a positive diagnosis i.e. breast cancer.

In an alternative example, the fragments produced by the restriction enzyme are detected using an amplification system, such as, for example, polymerase chain reaction (PCR), rolling circle amplification (RCA), inverse polymerase chain reaction (iPCR), in situ PCR (Singer-Sam et al., Nucl. Acids Res. 18, 687,1990), strand displacement amplification (SDA) or cycling probe technology.

Methods of PCR are known in the art and described, for example, by McPherson et al., PCR: A Practical Approach. (series eds, D. Rickwood and B. D. Hames), IRL Press Limited, Oxford. pp1-253, 1991 and by Dieffenbach (ed) and Dveksler (ed) (In: PCR Primer: A Laboratory Manual, Cold Spring Harbour Laboratories, NY, 1995), the contents of which are each incorporated in their entirety by way of reference. Generally, for PCR two non-complementary nucleic acid primer molecules comprising at least about 18 nucleotides in length, and more preferably at least 20-30 nucleotides in length are hybridized to different strands of a nucleic acid template molecule at their respective annealing sites, and specific nucleic acid molecule copies of the template that intervene the annealing sites are amplified enzymatically. Amplification products may be detected, for example, using electrophoresis and detection with a detectable marker that binds nucleic acids. Alternatively, one or more of the oligonucleotides are labeled with a detectable marker (e.g. a fluorophore) and the amplification product detected using, for example, a lightcycler (Perkin Elmer, Wellesley, Mass., USA).

Strand displacement amplification (SDA) utilizes oligonucleotide primers, a DNA polymerase and a restriction endonuclease to amplify a target sequence. The oligonucleotides are hybridized to a target nucleic acid and the polymerase is used to produce a copy of the region intervening the primer annealing sites. The duplexes of copied nucleic acid and target nucleic acid are then nicked with an endonuclease that specifically recognizes a sequence at the beginning of the copied nucleic acid. The DNA polymerase recognizes the nicked DNA and produces another copy of the target region at the same time displacing the previously generated nucleic acid. The advantage of SDA is that it occurs in an isothermal format, thereby facilitating high-throughput automated analysis.

Cycling Probe Technology uses a chimeric synthetic primer that comprises DNA-RNA-DNA that is capable of hybridizing to a target sequence. Upon hybridization to a target sequence the RNA-DNA duplex formed is a target for RNaseH thereby cleaving the primer. The cleaved primer is then detected, for example, using mass spectrometry or electrophoresis.

For primers that flank, or which are adjacent tom a methylation-sensitive endonuclease recognition site, it is preferred that such primers flank only those sites that are hypermethylated in the cancer to ensure that a diagnostic and/or prognostic amplification product is produced. In this regard, an amplification product will only be produced when the restriction site is not cleaved i.e., when it is methylated. Accordingly, detection of an amplification product indicates that the CpG dinucleotide/s of interest is/are methylated.

This form of analysis may be used to determine the methylation status of a plurality of CpG dinucleotides within a genomic region provided that each dinucleotide is within a methylation sensitive restriction endonuclease site.

In these methods, one or more of the primers may be labeled with a detectable marker to facilitate rapid detection of amplified nucleic acid, for example, a fluorescent label (e.g. Cy5 or Cy3) or a radioisotope (e.g. ³²P).

The amplified nucleic acids are generally analyzed using, for example, non-denaturing agarose gel electrophoresis, non-denaturing polyacrylamide gel electrophoresis, mass spectrometry, liquid chromatography (e.g. HPLC or dHPLC), or capillary electrophoresis. (e.g. MALDI-TOF). High throughput detection methods, such as, for example, matrix-assisted laser desorption/ionization time of flight (MALDI-TOF), electrospray ionization (ESI), mass spectrometry (including tandem mass spectrometry, e.g. LC MS/MS), biosensor technology, evanescent fiber-optics technology or DNA chip technology (e.g., WO98/49557; WO 96/17958; Fodor et al., Science 767-773, 1991; U.S. Pat. Nos. 5,143,854; and 5,837,832, the contents of which are all incorporated herein by reference).

Alternatively, amplification of a nucleic acid may be continuously monitored using a melting curve analysis method as described herein and/or in, for example, U.S. Pat. No. 6,174,670, which is incorporated herein by reference.

c) Selective Mutagenesis of Non-Methylated DNA

In an alternative example of the present disclosure, the methylation status of a genomic region in a subject sample is determined using a process comprising treating the nucleic acid with an amount of a compound that selectively mutates a non-methylated cytosine residue within a CpG dinucleotide under conditions sufficient to induce mutagenesis.

Exemplary compounds mutate cytosine to uracil or thymidine, such as, for example, a salt of bisulfite, e.g., sodium bisulfite or potassium bisulfite (Frommer et al., Proc. Natl. Acad. Sci. USA 89, 1827-1831, 1992). Bisulfite treatment of DNA is known to distinguish methylated from non-methylated cytosine residues, by mutating cytosine residues that are not protected by methylation, including cytosine residues that are not within a CpG dinucleotide or that are positioned within a CpG dinucleotide that is not subject to methylation.

(i) Sequence Based Detection

In one example, the presence of one or more mutated nucleotides in a genomic region or the number of mutated sequences in a sample is determined by sequencing mutated DNA. One form of analysis comprises amplifying mutated nucleic acid or methylated nucleic acid using an amplification reaction described herein, for example, PCR. The amplified product is then directly sequenced or cloned and the cloned product sequenced. Methods for sequencing DNA are known in the art and include for example, the dideoxy chain termination method or the Maxam-Gilbert method (see Sambrook et al., Molecular Cloning, A Laboratory Manual (2nd Ed., CSHP, New York 1989) or Zyskind et al., Recombinant DNA Laboratory Manual, (Acad. Press, 1988)).

As the treatment of nucleic acid with a compound, such as, for example, bisulfite results in non-methylated cytosines being mutated to uracil or thymidine, analysis of the sequence determines the presence or absence of a methylated nucleotide. For example, by comparing the sequence obtained using a control sample or a sample that has not been treated with bisulfite, or the known nucleotide sequence of the region of interest with a treated sample facilitates the detection of differences in the nucleotide sequence. Any thymine residue detected at the site of a cytosine in the treated sample compared to a control or untreated sample may be considered to be caused by mutation as a result of bisulfite treatment. Suitable methods for the detection of methylation using sequencing of bisulfite treated nucleic acid are described, for example, in Frommer et al., Proc. Natl. Acad. Sci. USA 89: 1827-1831, 1992 or Clark et al., Nucl. Acids Res. 22: 2990-2997, 1994. One example of a commercially available kit for carrying out such methods is the CpGenome™ DNA modification Kit (Millipore). Other suitable kits are available from MDX Health SA (Belgium).

In another example, the presence of a mutated or non-mutated nucleotide in a bisulfite treated sample is detected using pyrosequencing, such as, for example, as described in Uhlmann et al., Electrophoresis, 23: 4072-4079, 2002. Essentially this method is a form of real-time sequencing that uses a primer that hybridizes to a site adjacent or close to the site of a cytosine that is methylated in a cancer cell. Following hybridization of the primer and template in the presence of a DNA polymerase each of four modified deoxynucleotide triphosphates are added separately according to a predetermined dispensation order. Only an added nucleotide that is complementary to the bisulfite treated sample is incorporated and inorganic pyrophosphate (PPi) is liberated. The PPi then drives a reaction resulting in production of detectable levels of light. Such a method allows determination of the identity of a specific nucleotide adjacent to the site of hybridization of the primer.

A related method for determining the sequence of a bisulfite treated nucleotide is methylation-sensitive single nucleotide primer extension (Me-SnuPE) or SNaPmeth. Suitable methods are described, for example, in Gonzalgo and Jones Nucl. Acids Res., 25: 2529-2531 or Uhlmann et al., Electrophoresis, 23: 4072-4079, 2002.

Clearly other high throughput sequencing methods are encompassed by the present disclosure. Such methods include, for example, solid phase minisequencing (as described, for example, in Syvämen et al, Genomics, 13: 1008-1017, 1992), or minisequencing with FRET (as described, for example, in Chen and Kwok, Nucleic Acids Res. 25: 347-353, 1997).

(ii) Restriction Endonuclease-Based Assay Format

In one example, the presence of a non-mutated nucleic sequence is detected using combined bisulfite restriction analysis (COBRA) essentially as described in Xiong and Laird, Nucl. Acids Res., 25: 2532-2534, 2001. This method exploits the differences in restriction enzyme recognition sites between methylated and unmethylated nucleic acid after treatment with a compound that selectively mutates a non-methylated cytosine residue, e.g., bisulfite.

Following bisulfite treatment a region of interest comprising one or more CpG dinucleotides that are methylated in a cancer cell and are included in a restriction endonuclease recognition sequence is amplified using an amplification reaction described herein, e.g., PCR. The amplified product is then contacted with the restriction enzyme that cleaves at the site of the CpG dinucleotide for a time and under conditions sufficient for cleavage to occur. A restriction site may be selected to indicate the presence or absence of methylation. For example, the restriction endonuclease TaqI cleaves the sequence TCGA, following bisulfite treatment of a non-methylated nucleic acid the sequence will be TTGA and, as a consequence, will not be cleaved. The digested and/or non-digested nucleic acid is then detected using a detection means known in the art, such as, for example, electrophoresis and/or mass spectrometry. The cleavage or non-cleavage of the nucleic acid is indicative of cancer in a subject.

Clearly, this method may be employed in either a positive read-out or negative read-out system for the diagnosis of a cancer.

(iii) Positive Read-Out Assay Format

In one example, the assay format of the disclosure comprises a positive read-out system in which DNA from a cancer sample e.g., breast cancer, that has been treated, for example, with bisulfite is detected as a positive signal. For example, the non-hypermethylated DNA from a healthy or normal control subject is not detected or only weakly detected.

In one example, the enhanced methylation in a subject sample is determined using a process comprising:

-   (i) treating the nucleic acid with an amount of a compound that     selectively mutates a non-methylated cytosine residue under     conditions sufficient to induce mutagenesis thereby producing a     mutated nucleic acid; -   (ii) hybridizing a nucleic acid to a probe or primer comprising a     nucleotide sequence that is complementary to a sequence comprising a     methylated cytosine residue under conditions such that selective     hybridization to the non-mutated nucleic acid occurs; and -   (iii) detecting the selective hybridization.

In this context, the term “selective hybridization” means that hybridization of a probe or primer to the non-mutated nucleic acid occurs at a higher frequency or rate, or has a higher maximum reaction velocity, than hybridization of the same probe or primer to the corresponding mutated sequence. Preferably, the probe or primer does not hybridize or detectably hybridize (e.g., does not hybridize at a level significantly above background) to the non-methylated sequence carrying the mutation(s) under the reaction conditions used.

In one example, the hybridization is detected using Southern, dot blot, slot blot or other nucleic acid hybridization means (Kawai et al., Mol. Cell. Biol. 14, 7421-7427, 1994; Gonzalgo et al., Cancer Res. 57, 594-599, 1997). Subject to appropriate probe selection, such assay formats are generally described herein above and apply mutatis mutandis to the presently described selective mutagenesis approach.

In one example, a ligase chain reaction format is employed to distinguish between a mutated and non-mutated nucleic acid. Ligase chain reaction (described in EP 320,308 and U.S. Pat. No. 4,883,750) uses at least two oligonucleotide probes that anneal to a target nucleic acid in such a way that they are juxtaposed on the target nucleic acid such that they can be linked using a ligase. The probes that are not ligated are removed by modifying the hybridization stringency. In this respect, probes that have not been ligated will selectively hybridize under lower stringency hybridization conditions than probes that have been ligated. Accordingly, the stringency of the hybridization can be increased to a stringency that is at least as high as the stringency used to hybridize the longer probe, and preferably at a higher stringency due to the increased length contributed by the shorter probe following ligation. One exemplary method melts the target-probe duplex, elute the dissociated probe and confirm that is has been ligated, e.g., by determining its length using electrophoresis, mass spectrometry, nucleotide sequence analysis, gel filtration, or other means known to the skilled artisan.

Methylation specific microarrays (MSO) are also useful for differentiating between a mutated and non-mutated sequence. A suitable method is described, for example, in Adorján et al, Nucl. Acids Res., 30: e21, 2002. MSO uses nucleic acid that has been treated with a compound that selectively mutates a non-methylated cytosine residue (e.g., bisulfite) as template for an amplification reaction that amplifies both mutant and non-mutated nucleic acid. The amplification is performed with at least one primer that comprises a detectable label, such as, for example, a fluorophore, e.g., Cy3 or Cy5. The labeled amplification products are then hybridized to oligonucleotides on the microarray under conditions that enable detection of single nucleotide differences. Following washing to remove unbound amplification product, hybridization is detected using, for example, a microarray scanner. Not only does this method allow for determination of the methylation status of a large number of CpG dinucleotides, it is also semi-quantitative, enabling determination of the degree of methylation at each CpG dinucleotide analyzed. As there may be some degree of heterogeneity of methylation in a single sample, such quantification may assist in the diagnosis of cancer.

In an alternative example, the hybridization is detected using an amplification system. In methylation-specific PCR formats (MSP; Herman et al. Proc. Natl. Acad. Sci. USA 93: 9821-9826, 1992), the hybridization is detection using a process comprising amplifying the bisulfite-treated DNA. By using one or more probe or primer that anneals specifically to the unmutated sequence under moderate and/or high stringency conditions an amplification product is only produced using a sample comprising a methylated nucleotide.

Any amplification assay format described herein can be used, such as, for example, polymerase chain reaction (PCR), rolling circle amplification (RCA), inverse polymerase chain reaction (iPCR), in situ PCR (Singer-Sam et al., Nucl. Acids Res. 18, 687, 1990), strand displacement amplification, or cycling probe technology.

PCR techniques have been developed for detection of gene mutations (Kuppuswamy et al., Proc. Natl. Acad. Sci. USA 88:1143-1147, 1991) and quantitation of allelic-specific expression (Szabo and Mann, Genes Dev. 9: 3097-3108, 1995; and Singer-Sam et al., PCR Methods Appl. 1: 160-163, 1992). Such techniques use internal primers, which anneal to a PCR-generated template and terminate immediately 5′ of the single nucleotide to be assayed. Such as format is readily combined with ligase chain reaction as described herein above.

Methylation-specific melting-curve analysis (essentially as described in Worm et al., Clin. Chem., 47: 1183-1189, 2001) is also contemplated by the present disclosure. This process exploits the difference in melting temperature in amplification products produced using bisulfite treated methylated or unmethylated nucleic acid. In essence, non-discriminatory amplification of a bisulfite treated sample is performed in the presence of a fluorescent dye that specifically binds to double stranded DNA (e.g., SYBR Green I). By increasing the temperature of the amplification product while monitoring fluorescence the melting properties and thus the sequence of the amplification product is determined. A decrease in the fluorescence reflects melting of at least a domain in the amplification product. The temperature at which the fluorescence decreases is indicative of the nucleotide sequence of the amplified nucleic acid, thereby permitting the nucleotide at the site of one or more CpG dinucleotides to be determined. As the sequence of the nucleic acids amplified using the present disclosure

The present disclosure also encompasses the use of real-time quantitative forms of PCR, such as, for example, TaqMan (Holland et al., Proc. Natl Acad. Sci. USA, 88, 7276-7280, 1991; Lee et al., Nucleic Acid Res. 21, 3761-3766, 1993) to perform this embodiment. For example, the MethylLight method of Eads et al., Nucl. Acids Res. 28: E32, 2000 uses a modified TaqMan assay to detect methylation of a CpG dinucleotide.

Alternatively, rather than using a labeled probe that requires cleavage, a probe, such as, for example, a Molecular Beacon™ is used (see, for example, Mhlang and Malmberg, Methods 25: 463-471, 2001). Molecular beacons are single stranded nucleic acid molecules with a stem-and-loop structure. The loop structure is complementary to the region surrounding the one or more CpG dinucleotides that are methylated in a cancer sample and not in a control sample. The stem structure is formed by annealing two “arms” complementary to each other, which are on either side of the probe (loop). A fluorescent moiety is bound to one arm and a quenching moiety that suppresses any detectable fluorescence when the molecular beacon is not bound to a target sequence is bound to the other arm. Upon binding of the loop region to its target nucleic acid the arms are separated and fluorescence is detectable. However, even a single base mismatch significantly alters the level of fluorescence detected in a sample. Accordingly, the presence or absence of a particular base is determined by the level of fluorescence detected. Such an assay facilitates detection of one or more unmutated sites (i.e. methylated nucleotides) in a nucleic acid.

As exemplified herein, another amplification based assay useful for the detection of a methylated nucleic acid following treatment with a compound that selectively mutates a non-methylated cytosine residue makes use of headloop PCR technology (e.g., as described in published PCT Application No. PCT/AU03/00244; WO 03/072810). This form of amplification uses a probe or primer that comprises a region that binds to a nucleic acid and is capable of amplifying nucleic acid in an amplification reaction whether the nucleic acid is methylated or not. The primer additionally comprises a region that is complementary to a portion of the amplified nucleic acid enabling this region of the primer to hybridize to the amplified nucleic acid incorporating the primer thereby forming a hairpin. The now 3′ terminal nucleotide/s of the annealed region (i.e. the most 5° nucleotide's of the primer) hybridize to the site of one or more mutated cytosine residues (i.e., unmethylated in nucleic acid from a cancer subject). Accordingly, this facilitates self priming of amplification products from unmethylated nucleic acid, the thus formed hairpin structure blocking further amplification of this nucleic acid. In contrast, the complementary region may or may not by capable of hybridizing to an amplification product from methylated (mutated) nucleic acid, but is unable to “self prime” thereby enabling further amplification of this nucleic acid (e.g., by the inability of the now 3′ nucleotide to hybridize to the amplification product). This method may be performed using a melting curve analysis method to determine the amount of methylated nucleic acid in a biological sample from a subject.

Other amplification based methods for detecting methylated nucleic acid following treatment with a compound that selectively mutates a non-methylated cytosine residue include, for example, methylation-specific single stranded conformation analysis (MS-SSCA) (Bianco et al., Hum. Mutat., 14: 289-293, 1999), methylation-specific denaturing gradient gel electrophoresis (MS-DGGE) (Abrams and Stanton, Methods Enzymol., 212: 71-74, 1992) and methylation-specific denaturing high-performance liquid chromatography (MS-DHPLC) (Deng et al, Chin. J. Cancer Res., 12: 171-191, 2000). Each of these methods use different techniques for detecting nucleic acid differences in an amplification product based on differences in nucleotide sequence and/or secondary structure. Such methods are clearly contemplated by the present disclosure.

(iv) Negative Read-Out Assays

In an alternative example, the assay format comprises a negative read-out system in which reduced methylation of DNA from a healthy/normal control sample is detected as a positive signal and preferably, methylated DNA from a cancer sample e.g., a breast cancer sample, is not detected or is only weakly detected.

In one example, the reduced methylation is determined using a process comprising:

-   (i) treating the nucleic acid with an amount of a compound that     selectively mutates a non-methylated cytosine residue within a CpG     island under conditions sufficient to induce mutagenesis thereby     producing a mutated nucleic acid; -   (ii) hybridizing the nucleic acid to a probe or primer comprising a     nucleotide sequence that is complementary to a sequence comprising     the mutated cytosine residue under conditions such that selective     hybridization to the mutated nucleic acid occurs; and -   (iii) detecting the selective hybridization.

In this context, the term “selective hybridization” means that hybridization of a probe or primer to the mutated nucleic acid occurs at a higher frequency or rate, or has a higher maximum reaction velocity, than hybridization of the same probe or primer to the corresponding non-mutated sequence. In one example, the probe or primer does not hybridize or detectably hybridize to the methylated sequence (or non-mutated sequence) under the reaction conditions used.

The skilled artisan will be able to adapt a positive read-out assay described above to a negative read-out assay, e.g., by producing a probe or primer that selectively hybridizes to non-mutated DNA rather than mutated DNA.

d) Additional Method Steps

The methods disclosed herein may further comprise one or more steps of enriching methylated DNA in a sample. Thus, the methods disclosed herein may further comprise one or more steps of isolating methylated DNA from a sample. The enrichment/isolation step may be performed prior to or concomitant with any other step in the method for detecting the level of methylation of a marker as disclosed herein.

Any suitable enriching/isolating step known in the art may be used. For example, the methods disclosed herein may comprise a step of enriching methylated DNA in a sample using a commercially available kit such as the CpG MethylQuest DNA Isolation Kit (Millipore), which provides a recombinant protein comprising the methyl binding domain (MBD) of the mouse MBD2b protein fused to a glutathione-S-transferase (GST) protein from S. japonicum via a linker containing a thrombin cleavage site, the recombinant protein being immobilized to a magnetic bead. The MBD binds to methylated CpG sites with high affinity and in a sequence-independent manner, thereby allowing enrichment of methylated DNA in a sample.

It will be appreciated that alternative or additional methods known in the art for enrichment/isolation of methylated DNA in a sample can be used in the methods disclosed herein. For example, methods of enrichment/isolation of methylated DNA in a sample are described in Hsu et al., (2014) Methods Mol Biol, 1105:61-70, Serre et al., (2010) Nucleic Acids Res, 38:391-399, Rauch and Pfeifer (2005) Lab Invest, 85:1172-1180, Nair et al., (2011) Epigenetics, 6:34-44; and Robinson et al., (2010) Genome Res, 20:1719-1729.

A method disclosed herein according to any example may also comprise selecting a patient based on the result of a method disclosed herein and performing an additional diagnostic method or recommending performance of an additional diagnostic method. For example, for a patient diagnosed as suffering from breast cancer, the additional diagnostic method may be an ultrasound or a biopsy.

2. Detection of Reduced Gene Expression

Since methylation of a nucleic acid sequence affects its expression, the present inventors have also demonstrated that the level of expression of nucleic acids within any of a number of genomic regions described herein is varied (e.g., reduced or increased) in breast cancer subjects and in breast cancer cell lines. Thus, the methods disclosed herein may additionally or alternatively comprise determining the level of expression of any polynucleotides within any of the genomic regions identified in any of the Tables herein.

a) Nucleic Acid Detection

In one example, the level of expression of a nucleic acid is determined by detecting the level of mRNA transcribed from genomic region described herein.

In one example, the mRNA is detected by hybridizing a nucleic acid probe or primer capable of specifically hybridizing to a transcript of a genomic region described herein to a nucleic acid in a biological sample derived from a subject and detecting the hybridization by a detection means. Preferably, the detection means is an amplification reaction, or a nucleic acid hybridization reaction, such as, for example, as described herein.

In this context, the term “selective hybridization” means that hybridization of a probe or primer to the transcript occurs at a higher frequency or rate, or has a higher maximum reaction velocity, than hybridization of the same probe or primer to any other nucleic acid. Preferably, the probe or primer does not hybridize to another nucleic acid at a detectable level under the reaction conditions used.

As transcripts of a gene or pseudogene described herein are detected using mRNA or cDNA derived therefrom, assays that detect changes in mRNA are preferred (e.g. Northern hybridization, RT-PCR, NASBA, TMA or ligase chain reaction).

Methods of RT-PCR are known in the art and described, for example, in Dieffenbach (ed) and Dveksler (ed) (In: PCR Primer: A Laboratory Manual, Cold Spring Harbour Laboratories, NY, 1995). Essentially, this method comprises performing a PCR reaction using cDNA produced by reverse transcribing mRNA from a cell using a reverse transcriptase. Methods of PCR described supra are to be taken to apply mutatis mutandis to this embodiment of the disclosure.

Similarly LCR may be performed using cDNA. Preferably, one or more of the probes or primers used in the reaction specifically hybridize to the transcript of interest. Method of LCR are described supra and are to be taken to apply mutatis mutandis to this embodiment of the disclosure.

Methods of TMA or self-sustained sequence replication (35R) use two or more oligonucleotides that flank a target sequence, a RNA polymerase, RNase H and a reverse transcriptase. One oligonucleotide (that also comprises a RNA polymerase binding site) hybridizes to an RNA molecule that comprises the target sequence and the reverse transcriptase produces cDNA copy of this region. RNase H is used to digest the RNA in the RNA-DNA complex, and the second oligonucleotide used to produce a copy of the cDNA. The RNA polymerase is then used to produce a RNA copy of the cDNA, and the process repeated.

NASBA systems relies on the simultaneous activity of three enzymes (a reverse transcriptase, RNase H and RNA polymerase) to selectively amplify target mRNA sequences. The mRNA template is transcribed to cDNA by reverse transcription using an oligonucleotide that hybridizes to the target sequence and comprises a RNA polymerase binding site at its 5′ end. The template RNA is digested with RNase H and double stranded DNA is synthesized. The RNA polymerase then produces multiple RNA copies of the cDNA and the process is repeated.

The present disclosure also contemplates the use of a microarray to determine the level of expression of one or more nucleic acids described herein. Such a method enables the detection of a number of different nucleic acids, thereby providing a multi-analyte test and improving the sensitivity and/or accuracy of the diagnostic assay of the disclosure.

b) Polypeptide Detection

In an alternative example, the level of expression of a genomic region is determined by detecting the level of a protein encoded by a nucleic acid within a genomic region described herein.

In this respect, the present disclosure is not necessarily limited to the detection of a protein comprising the specific amino acid sequence recited herein. Rather, the present disclosure encompasses the detection of variant sequences (e.g., having at least about 80% or 90% or 95% or 98% amino acid sequence identity) or the detection of an immunogenic fragment or epitope of said protein.

The amount, level or presence of a polypeptide is determined using any of a variety of techniques known to the skilled artisan such as, for example, a technique selected from the group consisting of, immunohistochemistry, immunofluorescence, an immunoblot, a Western blot, a dot blot, an enzyme linked immunosorbent assay (ELISA), radioimmunoassay (RIA), enzyme immunoassay, fluorescence resonance energy transfer (FRET), matrix-assisted laser desorption/ionization time of flight (MALDI-TOF), electrospray ionization (ESI), mass spectrometry (including tandem mass spectrometry, e.g. LC MS/MS), biosensor technology, evanescent fiber-optics technology or protein chip technology.

In one example, the assay used to determine the amount or level of a protein is a semi-quantitative assay. In another example, the assay used to determine the amount or level of a protein in a quantitative assay. As will be apparent from the preceding description, such an assay may require the use of a suitable control, e.g. from a normal individual or matched normal control.

Standard solid-phase ELISA or FLISA formats are particularly useful in determining the concentration of a protein from a variety of samples.

In one form such an assay involves immobilizing a biological sample onto a solid matrix, such as, for example a polystyrene or polycarbonate microwell or dipstick, a membrane, or a glass support (e.g. a glass slide). An antibody that specifically binds to a protein described herein is brought into direct contact with the immobilized biological sample, and forms a direct bond with any of its target protein present in said sample. This antibody is generally labeled with a detectable reporter molecule, such as for example, a fluorescent label (e.g. FITC or Texas Red) or a fluorescent semiconductor nanocrystal (as described in U.S. Pat. No. 6,306,610) in the case of a FLISA or an enzyme (e.g. horseradish peroxidase (HRP), alkaline phosphatase (AP) or β-galactosidase) in the case of an ELISA, or alternatively a second labeled antibody can be used that binds to the first antibody. Following washing to remove any unbound antibody the label is detected either directly, in the case of a fluorescent label, or through the addition of a substrate, such as for example hydrogen peroxide, TMB, or toluidine, or 5-bromo-4-chloro-3-indol-beta-D-galaotopyranoside (x-gal) in the case of an enzymatic label.

In another form, an ELISA or FLISA comprises immobilizing an antibody or ligand that specifically binds a protein described supra on a solid matrix, such as, for example, a membrane, a polystyrene or polycarbonate microwell, a polystyrene or polycarbonate dipstick or a glass support. A sample is then brought into physical relation with said antibody, and the polypeptide is bound or ‘captured’. The bound protein is then detected using a labeled antibody. For example, a labeled antibody that binds to an epitope that is distinct from the first (capture) antibody is used to detect the captured protein. Alternatively, a third labeled antibody can be used that binds the second (detecting) antibody.

In another example, the presence or level of a protein is detected in a body fluid using, for example, a biosensor instrument (e.g., BIAcore™, Pharmacia Biosensor, Piscataway, N.J.). In such an assay, an antibody or ligand that specifically binds a protein is immobilized onto the surface of a receptor chip. For example, the antibody or ligand is covalently attached to dextran fibers that are attached to gold film within the flow cell of the biosensor device. A test sample is passed through the cell. Any antigen present in the body fluid sample, binds to the immobilized antibody or ligand, causing a change in the refractive index of the medium over the gold film, which is detected as a change in surface plasmon resonance of the gold film.

In another example, the presence or level of a protein or a fragment or epitope thereof is detected using a protein and/or antibody chip. To produce such a chip, an antibody or ligand that binds to the antigen of interest is bound to a solid support such as, for example glass, polycarbonate, polytetrafluoroethylene, polystyrene, silicon oxide, gold or silicon nitride. This immobilization is either direct (e.g. by covalent linkage, such as, for example, Schiff's base formation, disulfide linkage, or amide or urea bond formation) or indirect.

To bind a protein to a solid support it is often necessary to treat the solid support so as to create chemically reactive groups on the surface, such as, for example, with an aldehyde-containing silane reagent or the calixcrown derivatives described in Lee et al, Proteomics, 3: 2289-2304, 2003. A streptavidin chip is also useful for capturing proteins and/or peptides and/or nucleic acid and/or cells that have been conjugated with biotin (e.g. as described in Pavlickova et al., Biotechniques, 34: 124-130, 2003). Alternatively, a peptide is captured on a microfabricated polyacrylamide gel pad and accelerated into the gel using microelectrophoresis as described in, Arenkov et al. Anal. Biochem. 278:123-131, 2000.

Other assay formats are also contemplated, such as flow-trough immunoassays (PCT/AU2002/01684), a lateral flow immunoassay (US20040228761, US20040248322 or US20040265926), a fluorescence polarization immunoassay (FPIA) (U.S. Pat. Nos. 4,593,089, 4,492,762, 4,668,640, and 4,751,190), a homogeneous microparticles immunoassay (“HMI”) (e.g., U.S. Pat. Nos. 5,571,728, 4,847,209, 6,514,770, and 6,248,597) or a chemiluminescent microparticle immunoassay (“CMIA”).

3 Multiplex Assay Formats

The present disclosure also contemplates multiplex or multianalyte format assays to improve the accuracy or specificity of a diagnosis or prognosis of breast cancer. Such assays may also improve the population coverage by an assay.

Methods for determining the sensitivity of an assay will be apparent to the skilled artisan. For example, an assay described herein is used to analyze a population of test subjects to determine those that will develop cancer. Post-mortem analysis is then used to determine those subjects that did actually determine breast cancer. The number of “true positives” (i.e., subjects that developed breast cancer and were positively identified using the method of the disclosure) and “true negatives” (i.e., subjects that did not develop breast cancer and were not identified using the method of the disclosure) are determined.

Sensitivity of the assay is then determined by the following formula: No. of true positives/(No. of true positives+No. of false negatives).

In one example, a method of the disclosure has a high degree of sensitivity in detecting of subjects developing or suffering from breast cancer. For example, in a test population of individuals, the assay detects at least about 50% of subjects developing or suffering from breast cancer, for example, at least about 60% of subjects developing or suffering from breast cancer, for example, at least about 65% of subjects developing or suffering from breast cancer, for example, at least about 70% of subjects developing or suffering from breast cancer, for example, at least about 75% of subjects developing or suffering from breast cancer, for example, at least about 80% of subjects developing or suffering from breast cancer, for example, at least about 85% of subjects developing or suffering from breast cancer, for example, at least about 90% of subjects developing or suffering from breast cancer, for example, at least about 95% of subjects developing or suffering from breast cancer.

In a more specific example, a method of the disclosure has a high degree of sensitivity in detecting of subjects developing or suffering from ER−ve breast cancer and/or TNBC. For example, in a test population of individuals, the assay detects at least about 50% of subjects developing or suffering from ER−ve breast cancer and/or TNBC, for example, at least about 60% of subjects developing or suffering from ER−ve breast cancer and/or TNBC, for example, at least about 65% of subjects developing or suffering from ER−ve breast cancer and/or TNBC, for example, at least about 70% of subjects developing or suffering from ER−ve breast cancer and/or TNBC, for example, at least about 75% of subjects developing or suffering from ER−ve breast cancer and/or TNBC, for example, at least about 80% of subjects developing or suffering from ER−ve breast cancer and/or TNBC, for example, at least about 85% of subjects developing or suffering from ER−ve breast cancer and/or TNBC, for example, at least about 90% of subjects developing or suffering from ER−ve breast cancer and/or TNBC, for example, at least about 95% of subjects developing or suffering from ER−ve breast cancer and/or TNBC.

In another example, a method of the disclosure has a high degree of sensitivity in stratifying TNBC subtypes associated with distinct prognostic profiles e.g., such as populations of TNBC patients with high, medium or low risk disease outcomes. For example, in a test population of individuals having TNBC, the assay stratifies at least about 50% of subjects having TNBC according to a disease outcome, for example, at least about 60% of subjects having TNBC according to a disease outcome, for example, at least about 70% of subjects having TNBC according to a disease outcome, for example, at least about 80% of subjects having TNBC according to a disease outcome, for example, at least about 85% of subjects having TNBC according to a disease outcome, for example, at least about 90% of subjects having TNBC according to a disease outcome, for example, at least about 95% of subjects having TNBC according to a disease outcome. A disease outcome in accordance with this example is a likelihood that the breast cancer patient will survive at least 3 years from the time or diagnosis/prognosis, for example, at least 5 years from the time or diagnosis/prognosis, for example, at least 10 years from the time or diagnosis/prognosis.

Specificity is determined by the following formula: No. of true negatives/(No. of true negatives+No. of false positives).

An exemplary multiplex assay comprises, for example, detecting differential methylation of one or more CpG dinucleotides in a plurality of DMRs set forth in Tables 1-3. In one example, the method comprises detecting the level of methylation of one or more CpG dinucleotides in a plurality of DMRs set forth in Table 1 to diagnose breast cancer. In another example, the method comprises detecting the level of methylation of one or more CpG dinucleotides in a plurality of DMRs set forth in Table 2 to diagnose ER−ve breast cancer and/or TNBC. In yet another example, the method comprises detecting the level of methylation of one or more CpG dinucleotides in a plurality of DMRs set forth in Table 3 to stratify and/or predict disease outcome in a patient suffering from TNBC.

The multiplex assay of the disclosure is not to be limited to the detection of methylation at a single CpG dinucleotide within a region of interest i.e., each DMR. Rather, the present disclosure contemplates detection of methylation at a sufficient number of CpG dinucleotides in each nucleic acid to provide a diagnosis/prognosis. For example, the disclosure contemplates detection of methylation at 1 or 2 or 3 or 4 or 5 or 7 or 9 or 10 or 15 or 20 or 25 or 30 CpG dinculeotides in each nucleic acid i.e., each DMR. Preferably, the disclosure contemplates detection of methylation at more than 1 CpG dinculeotide in each nucleic acid i.e., each DMR.

As will be apparent from the foregoing description, a methylation specific microarray is amenable to such high density analysis. Previously, up to 232 CpG dinucleotides have been analyzed using such a microarray (Adorján et al., Nucl. Acids Res. 30: e21, 2002).

In another example, the method determines the level of expression of a gene comprising, or comprised in, at least one DMR set forth in Tables 1-3 to diagnose/prognose breast cancer. For example, the method determines the level of expression of a gene comprising, or comprised in, at least one DMR set forth in Table 1 to diagnose breast cancer. In another example, the method determines the level of expression of a gene comprising, or comprised in, at least one DMR set forth in Table 2 to diagnose ER−ve and/or TNBC. In another example, the method determines the level of expression of a gene comprising, or comprised in, at least one DMR set forth in Table 3 to stratify and/or predict disease outcome in a patient suffering from TNBC. The level of mRNA or protein may be detected. Alternatively, the level of mRNA transcribed from one or more genes and the level of one or more proteins expressed by the same or different genes may be determined.

Each of the previously described detection techniques can be used independently of one another to diagnose cancer. Accordingly, a single sample may be analyzed to determine the level of methylation of one or more CpG dinculeotides in one or more nucleic acids and the level of expression of one or more nucleic acids and/or proteins is also determined. In accordance with this example, enhanced methylation and reduced gene expression is indicative of cancer.

Based on the teachings provided herein, a variety of combinations of assays will be apparent to the skilled artisan.

The present disclosure also contemplates the use of a known diagnostic assay in combination with an assay described herein. For example, detection of a mutation in a BRCA gene using an assay described herein may be used to diagnose breast cancer.

Samples

A sample useful for the method of the present disclosure is, for example, from a tissue suspected of comprising a breast cancer or breast cancer cell. For example, the cell is from a region of a tissue thought to comprise a breast cancer or breast cancer cell. This does not exclude cells that have originated in a particular tissue but are isolated from a remote source.

The sample may be taken from a subject suspected of having or being at risk of developing breast cancer. For example, the subject may have a family history of cancer, may have been subjected to tests identifying elevated levels of BRCA gene (which, in one example, may be deemed to indicate an increased likelihood of having or being susceptible to developing breast cancer), or may have been subjected to any other test for detecting and/or determining the likelihood of developing any form of breast cancer. The sample may be taken from a subject who has been subjected to any combination of any known test for detecting and/or determining the likelihood of developing any form of breast cancer. Alternatively, the sample may be taken from a subject not previously suspected of having breast cancer.

In one example, the sample comprises a body fluid or a derivative of a body fluid or a body secretion. For example, the body fluid is selected from the group consisting of whole blood, urine, saliva, breast milk, pleural fluid, sweat, tears and mixtures thereof. An example of a derivative of a body fluid is selected from the group consisting of plasma, serum or buffy coat fraction. In one example, the sample comprises a whole blood sample, a serum sample or a plasma sample.

In one example DNA is isolated from either; whole blood, plasma, serum, peripheral blood mononucleated cells (PBMC) or enriched epithelial cells derived from the blood of patients diagnosed with breast cancer or healthy controls. DNA may then be bisulfite converted and gene-specific methylated sequences may be detected by either; methylation specific headloop suppression PCR, MALDI-TOF mass spectrometry (sequenom) or other bisulfite based PCR assay.

Preferably, the sample comprises a nucleated cell or an extract thereof. More preferably, the sample comprises a breast cancer cell or an extract thereof.

In another example, the sample comprises nucleic acid and/or protein from a breast cancer cell. The nucleic acid and/or protein may be separate need not be isolated with a cell, but rather may be from, for example, a lysed cell.

As the present disclosure is particularly useful for the early detection of breast cancer in the medium to long term, the term breast cancer cell is not to be limited by the stage of a cancer in the subject from which said breast cancer cell is derived (i.e. whether or not the patient is in remission or undergoing disease recurrence or whether or not the breast cancer is a primary tumor or the consequence of metastases). Nor is the term “breast cancer cell”, “cancer cell” or similar to be limited by the stage of the cell cycle of said cancer cell.

In one example, the sample comprises a cell or a plurality of cells derived from a breast.

In one example, the biological sample has been isolated previously from the subject. In accordance with this example, a method of the present disclosure is performed ex vivo. In such cases, the sample may be processed or partially processed into a nucleic acid sample that is substantially free of contaminating protein. All such examples are encompassed by the present disclosure.

Methods for isolating a sample from a subject are known in the art and include, for example, surgery, biopsy, collection of a body fluid, for example, by paracentesis or thoracentesis or collection of, for example, blood or a fraction thereof. All such methods for isolating a biological sample shall be considered to be within the scope of providing or obtaining a sample.

For example, in the case of a breast cancer, a sample is collected, for example, using a fine needle aspiration biopsy, a core needle biopsy, or a surgical biopsy.

It will be apparent from the preceding description that methods provided by the present disclosure involve a degree of quantification to determine elevated or enhanced methylation of nucleic acid in tissue that is suspected of comprising a cancer cell or metastases thereof, or reduced gene expression in tissue that is suspected of comprising a cancer cell or metastases thereof. Such quantification is readily provided by the inclusion of appropriate control samples in the assays as described below.

As will be apparent to the skilled artisan, when internal controls are not included in each assay conducted, the control may be derived from an established data set.

Data pertaining to the control subjects are selected from the group consisting of:

-   1. a data set comprising measurements of the degree of methylation     and/or gene expression for a typical population of subjects known to     have breast cancer, or a particular form of breast cancer e.g., such     as TNBC, that is currently being tested or a typical population of     subjects known to have breast cancer generally; -   2. a data set comprising measurements of the degree of methylation     and/or gene expression for the subject being tested wherein said     measurements have been made previously, such as, for example, when     the subject was known to be healthy or, in the case of a subject     having breast cancer, when the subject was diagnosed or at an     earlier stage in disease progression; -   3. a data set comprising measurements of the degree of methylation     and/or gene expression for a healthy individual or a population of     healthy individuals; -   4. a data set comprising measurements of the degree of methylation     and/or gene expression for a normal individual or a population of     normal individuals; and -   5. a data set comprising measurements of the degree of methylation     and/or gene expression from the subject being tested wherein the     measurements are determined in a matched sample.

In a preferred example, the data comprising measurements of the degree of methylation and/or gene expression for a healthy subject, individual or population pertains to healthy breast epithelial cell(s) from the subject, individual or population.

Those skilled in the art are readily capable of determining the baseline for comparison in any diagnostic/prognostic assay of the present disclosure without undue experimentation, based upon the teaching provided herein.

In the present context, the term “typical population” with respect to subjects known to have breast cancer shall be taken to refer to a population or sample of subjects diagnosed with a specific form of breast cancer that is representative of the spectrum of subjects suffering from breast cancer. This is not to be taken as requiring a strict normal distribution of morphological or clinicopathopathological parameters in the population, since some variation in such a distribution is permissible. Preferably, a “typical population” will exhibit a spectrum of subtypes of breast cancers at different stages of disease progression and with tumors at different stages and having different morphologies or degrees of differentiation.

In the present context, the term “healthy individual” shall be taken to mean an individual who is known not to suffer from breast cancer, such knowledge being derived from clinical data on the individual. It is preferred that the healthy individual is asymptomatic with respect to the any symptoms associated with breast cancer.

The term “normal individual” shall be taken to mean an individual having a normal level of methylation at a genomic region and/or gene expression as described herein in a particular sample derived from said individual.

As will be known to those skilled in the art, data obtained from a sufficiently large sample of the population will normalize, allowing the generation of a data set for determining the average level of a particular parameter. Accordingly, the level of methylation and/or gene expression as described herein can be determined for any population of individuals, and for any sample derived from said individual, for subsequent comparison to levels determined for a sample being assayed. Where such normalized data sets are relied upon, internal controls are preferably included in each assay conducted to control for variation.

The term “matched sample” shall be taken to mean that a control sample is derived from the same subject as the test sample is derived, at approximately the same point in time. In one example, the control sample provides little or no morphological and/or pathological indications of cancer. Matched samples are not applicable to blood-based or serum-based assays. Accordingly, it is preferable that the matched sample is from a region of the same tissue as the test sample e.g., breast tissue, such as breast epithelial tissue, however does not appear to comprise a cancer cell. For example, the matched sample does not include malignant cells or exhibit any symptom of the disease. For example, the sample comprises less than about 20% malignant cells, such as less than about 10% malignant cells, for example less than about 5% malignant cells, e.g., less than about 1% malignant cells. Morphological and pathological indications of malignant cells are known in the art and/or described herein.

For example, the differential methylation of one or more DMRs set forth in Tables 1-3 relative to the methylation status of a corresponding one or more DMRs of a control is indicative of a subject having breast cancer. Alternatively, or in addition, differential methylation of one or more DMRs set forth in Tables 1-3 relative to the methylation status a corresponding one or more DMRs of a control is indicative of a breast cancer patient's response to therapy or the progression or recurrence of disease or metastasis.

In an alternative example, the differential expression of a gene associated with one or more DMRs set forth in Tables 1-3 e.g., a gene comprised within the DMR or comprising the DMR, relative to a corresponding gene expression of a control is indicative of a subject having breast cancer. Alternatively, or in addition, differential expression of a gene associated with one or more DMRs set forth in Tables 1-3 e.g., a gene comprised within one of the DMR or comprising the DMR, relative to a corresponding gene expression of a control is indicative of a breast cancer patient's response to therapy or the progression or recurrence of disease or metastasis.

In one example, the level(s) of differential methylation of the one or more DMRs set forth in Tables 1-3 are subjected to multivariate analysis to create an algorithm which enables the determination of an index of probability of the presence or absence of breast cancer, or metastasis or progression of breast cancer or response to treatment. For example, the level(s) of differential methylation of the one or more DMRs set forth in Table 1 are subjected to multivariate analysis to create an algorithm which enables the determination of an index of probability of the presence or absence of breast cancer, or metastasis or progression of breast cancer or response to treatment. For example, the level(s) of differential methylation of the one or more DMRs set forth in Table 2 are subjected to multivariate analysis to create an algorithm which enables the determination of an index of probability of the presence or absence of ER−ve breast cancer and/or TNBC, or metastasis or progression of ER−ve breast cancer and/or TNBC. For example, the level(s) of differential methylation of the one or more DMRs set forth in Table 3 are subjected to multivariate analysis to create an algorithm which enables determination of an index of probability of the presence or absence of TNBC, stratification of TNBC subtypes according to prognostic risk profiles, determination or prediction of metastasis or prediction of progression of TNBC to a worsening stage. Hence, in one example, the present disclosure provides a rule based on the application of a comparison of levels of methylation biomarkers to control samples. In another example, the rule is based on application of statistical and machine learning algorithms. Such an algorithm uses the relationships between methylation biomarkers and disease status observed in training data (with known disease status) to infer relationships which are then used to predict the status of patients with unknown status. Practitioners skilled in the art of data analysis recognize that many different forms of inferring relationships in the training data may be used without materially changing the present disclosure.

The term “status” shall be taken to include whether or not a subject suffers from breast cancer (i.e., diagnostic status), including breast cancer subtype (e.g., ER−ve breast cancer or TNBC), whether or not a breast cancer has progressed, whether or not a cancer has metastasized, and/or whether or not a subject is responding to treatment for a breast cancer.

Analysis as described in the preceding paragraphs can also consider clinical parameters or traditional laboratory risk factors.

Information as discussed above can be combined and made more clinically useful through the use of various formulae, including statistical classification algorithms and others, combining and in many cases extending the performance characteristics of the combination beyond that of any individual data point. These specific combinations show an acceptable level of diagnostic/prognostic accuracy, and, when sufficient information from multiple markers is combined in a trained formula, often reliably achieve a high level of diagnostic/prognostic accuracy transportable from one population to another.

Several statistical and modeling algorithms known in the art can be used to both assist in biomarker selection choices and optimize the algorithms combining these choices. Statistical tools such as factor and cross-biomarker correlation/covariance analyses allow more rational approaches to panel construction. Mathematical clustering and classification tree showing the Euclidean standardized distance between the biomarkers can be advantageously used. Pathway informed seeding of such statistical classification techniques also may be employed, as may rational approaches based on the selection of individual biomarkers (e.g., such as those DMRs set forth in Tables 1-3) based on their participation across in particular pathways or physiological functions or individual performance.

Ultimately, formulae such as statistical classification algorithms can be directly used to both select methylation biomarkers and to generate and train the optimal formula necessary to combine the results from multiple methylation biomarkers into a single index. Often techniques such as forward (from zero potential explanatory parameters) and backwards selection (from all available potential explanatory parameters) are used, and information criteria are used to quantify the tradeoff between the performance and diagnostic/prognostic accuracy of the panel and the number of methylation biomarkers used. The position of the individual methylation biomarkers on a forward or backwards selected panel can be closely related to its provision of incremental information content for the algorithm, so the order of contribution is highly dependent on the other constituent biomarkers in the panel.

Any formula may be used to combine methylation biomarker results into indices or indexes useful in the practice of the disclosure. As indicated herein, and without limitation, such indices may indicate, among the various other indications, the probability, likelihood, absolute or relative risk, time to or rate of disease, conversion from one to another disease states, or make predictions of future biomarker measurements of cancer. This may be for a specific time period or horizon, or for remaining lifetime risk, or simply be provided as an index relative to another reference subject population.

The actual model type or formula used may itself be selected from the field of potential models based on the performance and diagnostic accuracy characteristics of its results in a training population. The specifics of the formula itself may commonly be derived from biomarker results in the relevant training population. Amongst other uses, such formula may be intended to map the feature space derived from one or more biomarker inputs to a set of subject classes (e.g. useful in predicting class membership of subjects as normal, at risk for having breast cancer, recurrence or metastasis thereof or responding/not-responding to treatment), to derive an estimation of a probability function of risk using a Bayesian approach (e.g. the risk of breast cancer or a metastatic or recurrence event), or to estimate the class-conditional probabilities, then use Bayes' rule to produce the class probability function as in the previous case.

Following analysis and determination of an index of probability of the presence or absence of breast cancer (e.g., ER−ve breast cancer and/or TNBC), or metastasis or progression of breast cancer or response to treatment, the index can be transmitted or provided to a third party, e.g., a medical practitioner for assessment. The index may be used by the practitioner to assess whether or not additional diagnostic methods are required, e.g., biopsy and histological analysis and/or other assays, or a change in treatment or commencement of treatment.

Monitoring the Progression of Cancer

As the level of a biomarker of breast cancer varies with the progression of cancer, the methods described herein are useful for monitoring the progression of breast cancer in a subject suffering therefrom. In this regard, the term “determining the progression of cancer” includes determining the stage or grade of the breast cancer. For example, the method comprises determining differential methylation of one or more genomic regions set forth in Table 1, Table 2 and/or Table 3 in a sample from a subject relative to a reference level of methylation for the corresponding one or more genomic regions previously determined for the subject or a control sample. Enhanced differential methylation i.e., a further increased or further reduced level of methylation, of a genomic region in the sample compared to the previously obtained sample indicates that the disease has progressed, e.g., the disease may have progressed to a more advanced stage or may have advanced from pre-clinical to clinical. In a particularly preferred example, the method comprises determining differential methylation of one or more genomic regions set forth in Table 3 in a sample from a subject suffering from TNBC relative to a reference level of methylation for the corresponding one or more genomic regions previously determined for the subject or a control sample. Comparison to a control sample from a subject having a specific stage or grade permits identification of the stage or grade of the breast cancer e.g., such as TNBC, in the subject.

The present disclosure is also useful for determining the degree or risk of metastasis of breast cancer, for example, by determining the stage of the breast cancer. For example, the present disclosure is useful for determining metastasis of a breast cancer to a tissue, such as, for example, a lymph node, bone or lung.

Clearly, the detection of one or more additional biomarkers other than those set forth in Tables 1-3 is encompassed by this example of the disclosure.

Methods for detecting markers are described herein and are to be taken to apply mutatis mutandis to this example of the disclosure.

Monitoring the Efficacy of Treatment

As the method of the disclosure is useful for monitoring or determining the progression (e.g., stage) of breast cancer, it is also useful for determining the efficacy of a therapy for said disease.

For example, a method described herein is used to determine methylation status of one or more CpG dinucleotides within one or more genomic regions set forth in Table 1-3 in sample from a subject receiving treatment for breast cancer. This methylation status is then compared to, for example, methylation status for a healthy or control subject. Detection of differential methylation of one or more CpG dinucleotides within one or more genomic regions set forth in Table 1-3 in the test sample relative to a level of methylation for the corresponding genomic regions in the healthy or control sample is indicative that the subject is not responding to treatment. A similar level of methylation in the test sample and a healthy or control sample indicates that the subject is responding to or has responded to treatment for said disease.

In another example, the control sample is derived from a subject suffering from breast cancer or from the subject prior to commencing treatment or from a point in time earlier in the treatment. In this respect, a reduced level of differential methylation of the one or more CpG dinucleotides within one or more genomic regions set forth in Table 1-3 in the test sample compared to the control sample indicates that the subject is responding to or has responded to treatment. An enhanced or similar level of differential methylation of the one or more CpG dinucleotides within one or more genomic regions set forth in Table 1-3 in the test sample compared to the control sample indicates that the subject is not or has not responded to treatment.

Determining the Time to an Event

The method of the present disclosure is also useful for determining, for example, the risk of an event occurring, or the timing to an event occurring. For example, the present disclosure is useful for determining the risk of a patient dying early as a result of breast cancer e.g., as a result of TNBC, or determining the risk of metastasis or the timing to metastasis.

Such methods are also applicable to determining, for example, the risk of or time to development of one or more of the following:

(i) onset of clinical breast cancer;

(ii) the progression of breast cancer from one stage to another; or

(iii) the likelihood of response of a subject to a therapeutic or prophylactic agent.

To determine the time to an event or the risk of an event, e.g., the time to death of a subject, the level of a methylation biomarker of the disclosure is determined in a series of subjects for which survival data is known. A Cox Proportional Hazards model (see, e.g. Cox and Oakes (1984), Analysis of Survival Data, Chapman and Hall, London, N.Y.) is defined with time to death or early death as the dependent variable, and the level of the marker detected as the independent variable. The Cox model provides the relative risk (RR) of death for a unit change in the level of the marker. The subjects may then be partitioned into subgroups at any threshold value of the level of the marker (on the CT scale), where all subjects with levels above the threshold have higher risk, and all patients with levels below the threshold have lower risk of death or time to death, or vice versa, depending on whether the marker is an indicator of bad (RR>1.01) or good (RR<1.01) prognosis. Thus, any threshold value will define subgroups of patients with respectively increased or decreased risk.

The Cox proportional hazard model is the most general of the regression models because it is not based on any assumptions concerning the nature or shape of the underlying survival distribution. The model assumes that the underlying hazard rate (rather than survival time) is a function of the independent variables (covariates); no assumptions are made about the nature or shape of the hazard function.

In another embodiment, a Cox's Proportional Hazard Model with Time-Dependent Covariates is used to determine the time to or risk of an event in cancer based on a marker described herein in a sample from a subject. An assumption of the proportional hazard model is that the hazard function for an individual (i.e., observation in the analysis) depends on the values of the covariates and the value of the baseline hazard. Given two individuals with particular values for the covariates, the ratio of the estimated hazards over time will be constant.

Other methods for determining the time to or risk of an event will be apparent to the skilled artisan and include, for example, exponential regression, normal regression, log-normal regression or stratified analysis.

Using any of these forms of analysis a level of detection of a methylation biomarker is determined that is predictive of the risk or time to an event. For example, a level of differential methylation of one or more CpG dinucleotides within one or more genomic regions set forth in Table 3 in a TNBC sample relative to a reference level of methylation for the respective genomic regions, is predictive that a subject is likely to live for fewer than a predetermined number of years. For example, fewer than 5 years from the time of diagnosis/prognosis, or fewer than 3 years from the time of diagnosis/prognosis. In one example, a level of differential methylation of one or more CpG dinucleotides within one or more genomic regions set forth in rows 1-14 of Table 3 in a TNBC sample relative to a reference level of methylation for the respective genomic regions, is predictive that a subject is likely to live for fewer than 5 years from the time of diagnosis/prognosis, such as fewer than 3 years from the time of diagnosis/prognosis. In another example, a level of differential methylation of one or more CpG dinucleotides within one or more genomic regions set forth in rows 15-17 of Table 3 in a TNBC sample relative to a reference level of methylation for the respective genomic regions, is predictive that a subject is likely to live for greater than 3 years from the time of diagnosis/prognosis, such as greater than 5 years from the time of diagnosis/prognosis.

This form of analysis is useful for determining the risk of an event occurring in a subject or the time to an event occurring in a subject.

Accordingly, one example of the disclosure provides a method of determining a time to an event in breast cancer e.g., TNBC, or the risk of an event occurring in a breast cancer subject e.g., a TNBC subject, by determining a level of differential methylation of one or more CpG dinucleotides within one or more genomic regions set forth in Table 3 in a TNBC sample relative to a reference level of methylation for the respective genomic regions, wherein an enhanced level methylation or reduced level of methylation relative to the reference level is indicative of the time to an event in the breast cancer.

Methods of Treatment

The present disclosure additionally provides a method of treatment of breast cancer. Such a method comprises, for example diagnosing breast cancer using a method of the disclosure described in any one or more examples described herein and administering a suitable therapeutic and/or prophylactic compound or performing surgery or recommending treatment with a suitable therapeutic/prophylactic agent or recommending performance of surgery.

Kits

The present disclosure additionally provides a kit for use in a method of the disclosure. In one embodiment, the kit comprises:

-   (i) one or more probes or primers (or isolated antibodies or     ligands) that specifically hybridize to a biomarker described herein     according to any example; and -   (ii) detection means.

In another example, a kit additionally comprises a reference sample. Such a reference sample may for example, be a polynucleotide sample derived from a sample isolated from one or more subjects suffering from breast cancer. Alternatively, a reference sample may comprise a sample isolated from one or more normal healthy individuals.

In one example, the kit comprises a probe or primer. In one example, the probe or primer that is capable of selectively hybridizing to a CpG dinucleotide of a genomic region described herein according to any example.

In those cases where the probe is not already available, they must be produced. Apparatus for such synthesis is presently available commercially, such as the Applied Biosystems 380A DNA synthesizer and techniques for synthesis of various nucleic acids are available in the literature. Methods for producing probes or primers are known in the art and/or described herein.

In one example, a probe or primer selectively hybridizes to a CpG dinucleotide of a genomic region set forth in Tables 1-3 that is selectively mutated by, for example, bisulphite treatment if the residue is not methylated. In another example, a probe or primer selectively hybridizes to a CpG dinucleotide of a genomic region set forth in Tables 1-3 that can be methylated in a breast cancer cell.

The kit may further comprise instructions for the detection of methylation levels of any of the target genes disclosed herein and for the comparison of those methylation levels with a reference level. The instructions may provide one or a series of cut-off values demarcating the likelihood of risk of a subject having, or being predisposed to breast cancer.

The present disclosure additionally provides a kit or an article of manufacture comprising a compound for therapeutic or prophylactic treatment of breast cancer packaged with instructions to perform a method substantially as described herein according to any example of the disclosure.

Knowledge-Based Systems

Knowledge-based computer software and hardware for implementing an algorithm of the disclosure also form part of the present disclosure. Such computer software and/or hardware are useful for performing a method of the disclosure. Thus, the present disclosure also provides software or hardware programmed to implement an algorithm that processes data obtained by performing the method of the disclosure via an univariate or multivariate analysis to provide a disease index value and provide or permit a diagnosis of cancer and/or determine progression or status of a breast cancer or determine whether or not a breast cancer has progressed or determine whether or not a subject is responding to treatment for breast cancer in accordance with the results of the disease index value in comparison with predetermined values.

In one example, a method of the disclosure may be used in existing knowledge-based architecture or platforms associated with pathology services. For example, results from a method described herein are transmitted via a communications network (e.g. the Internet) to a processing system in which an algorithm is stored and used to generate a predicted posterior probability value which translates to the index of disease probability or risk of recurrence or metastasis or responsiveness to treatment which is then forwarded to an end user in the form of a diagnostic or predictive report.

The method of the disclosure may, therefore, be in the form of a kit or computer-based system which comprises the reagents necessary to detect the concentration of the biomarkers and the computer hardware and/or software to facilitate determination and transmission of reports to a clinician.

The assay of the present disclosure permits integration into existing or newly developed pathology architecture or platform systems. For example, the present disclosure contemplates a method of allowing a user to determine the status of a subject with respect to a breast cancer, the method including:

(a) receiving data in the form of levels of differential methylation of one or more CpG dinucleotides within one or more genomic regions set forth in Tables 1-3 for a test sample relative to a reference level of methylation, optionally in combination with another marker of breast cancer; (b) processing the subject data via univariate and/or multivariate analysis to provide a disease index value; (c) determining the status of the subject in accordance with the results of the disease index value in comparison with predetermined values; and (d) transferring an indication of the status of the subject to the user via the communications network reference to the multivariate analysis includes an algorithm which performs the multivariate analysis function.

In one example, the method additionally includes:

(a) having the user determine the data using a remote end station; and

(b) transferring the data from the end station to the base station via the communications network.

The base station can include first and second processing systems, in which case the method can include:

(a) transferring the data to the first processing system;

(b) transferring the data to the second processing system; and

(c) causing the first processing system to perform the univariate or multivariate analysis function to generate the disease index value.

The method may also include:

(a) transferring the results of the univariate or multivariate analysis function to the first processing system; and

(b) causing the first processing system to determine the status of the subject.

In this case, the method also includes at least one of:

(a) transferring the data between the communications network and the first processing system through a first firewall; and

(b) transferring the data between the first and the second processing systems through a second firewall.

The second processing system may be coupled to a database adapted to store predetermined data and/or the univariate or multivariate analysis function, the method include: (a) querying the database to obtain at least selected predetermined data or access to the multivariate analysis function from the database; and

(b) comparing the selected predetermined data to the subject data or generating a predicted probability index.

The second processing system can be coupled to a database, the method including storing the data in the database.

The method can also include having the user determine the data using a secure array, the secure array of elements capable of determining the level of biomarker and having a number of features each located at respective position(s) on the respective code. In this case, the method typically includes causing the base station to:

(a) determine the code from the data;

(b) determine a layout indicating the position of each feature on the array; and

(c) determine the parameter values in accordance with the determined layout, and the data.

The method can also include causing the base station to:

(a) determine payment information, the payment information representing the provision of payment by the user; and

(b) perform the comparison in response to the determination of the payment information.

The present disclosure also provides a base station for determining the status of a subject with respect to a cancer, the base station including:

(a) a store method;

(b) a processing system, the processing system being adapted to:

(i) receive subject data from the user via a communications network;

(iii) determining the status of the subject in accordance with the results of the algorithmic function including the comparison; and

(c) output an indication of the status of the subject to the user via the communications network.

The processing system can be adapted to receive data from a remote end station adapted to determine the data.

The processing system may include:

(a) a first processing system adapted to:

(i) receive the data; and

(ii) determine the status of the subject in accordance with the results of the univariate or multivariate analysis function including comparing the data; and

(b) a second processing system adapted to:

(i) receive the data from the processing system;

(ii) perform the univariate or multivariate analysis function including the comparison; and

(iii) transfer the results to the first processing system.

The base station typically includes:

(a) a first firewall for coupling the first processing system to the communications network; and

(b) a second firewall for coupling the first and the second processing systems.

The processing system can be coupled to a database, the processing system being adapted to store the data in the database.

The present disclosure is now described further in the following non-limiting examples.

Example 1: Markers of Breast Cancer

1.1 Methods

1.1.1 Breast Cancer Tissue Samples

DNA was extracted from cells microdissected from human tissue samples representing normal breast and tumor breast. Fresh frozen (FF) and formalin fixed paraffin embedded (FFPE) tissue were obtained. Samples were classified as triple negative Grade 3 ductal adenocarcinomas. Details of the sample are presented in Table 4.

1.1.2 DNA Isolation

1.1.2.1 Formalin Fixed Paraffin Embedded (FFPE) Tissue Samples

DNA isolation from formalin fixed paraffin embedded (FFPE) tissue was performed using the Gentra Puregene Genomic DNA purification tissue kit according to the manufacturer's instructions (Qiagen). 5×1 mm cores or 5×10 um full faced sections were used for each extraction. The de-paraffinization step was carried out as follows: the paraffin samples was cut into small piece, 500 ul Xylene was added and incubated at 55° C. for 5 mins, and the tissue was pelleted at 16,000 g for 3 mins, discarding the Xylene. After repeating this step, 500 ul 100% EtOH was added for 5 mins at room temperature with constant mixing and the tissue collected by centrifugation @16,000 g for 3 mins. The EtOH step was repeated and the tissue pellet dried for 10 mins. 300 ul of cell lysis solution was added and the tube incubated for 70° C. for 10 mins, followed by addition of 20 ul Proteinase K (20 mg/ml) to each sample and vortexing for 20 secs and incubation in a 55° C. block overnight with constant vortexing. The following day a further 10 ul proteinase K was added, vortexed for 20 secs and further incubated at 55° C. until the samples appear clear. 1 ul RNase A solution (100 mg/ml) was added, mixed by inverting 25 times and incubated at 37° C. for 1 hr. The sample was placed on ice to quickly cool it. 100 ul protein precipitation solution was added to the cell lysates, which was then vortexed for 20 secs, incubated on ice for 5 mins, and centrifuged at full speed for 5 mins at 4° C. to pellet the protein precipitate. The supernatant containing the DNA was carefully removed into a clean microcentrifuge tube. The DNA was precipitated with 300 ul 100% isopropanol and 2 ul glycogen (20 mg/ml) were added if low yield was expected (<1 ug). The solutions were mixed by inversion (50 times) followed by centrifugation for 10 mins at 4° C. The DNA pellet was washed with 70% EtOH, air-dried and dissolved in 20 ul H₂O. To dissolve the pellet it was incubated for 1 hr at 65° C. with constant vortexing.

1.1.2.2 Formalin Fixed Paraffin Embedded (FETE) Tissue Samples

DNA from fresh frozen (FE) tissue was isolated using the Gentra Puregene Genomic DNA purification tissue kit according to the manufacturer's instructions (Qiagen). 5×1 mm cores or 5×10 um full faced sections were used for each extraction. Each sample was ground in a 1.5 ml tube. 300 ul of cell lysis solution was added and the tube incubated for 70° C. for 10 mins, followed by addition of 20 ul Proteinase K (20 mg/ml) to each sample and vortexing for 20 secs and incubation in a 55° C. block overnight with constant vortexing. The following day a further 10 ul proteinase K was added, vortexed for 20 secs and further incubated at 55° C. until the samples appear clear. 1 ul RNase A solution (100 mg/ml) was added, mixed by inverting 25 times and incubated at 37° C. for 1 hr. The sample was placed on ice to quickly cool it. 100 ul protein precipitation solution was added to the cell lysates, which was then vortexed for 20 secs, incubated on ice for 5 mins, and centrifuged at full speed for 5 mins at 4° C. to pellet the protein precipitate. The supernatant containing the DNA was carefully removed into a clean microcentrifuge tube. The DNA was precipitated with 300 ul 100% isopropanol and 2 ul glycogen (20 mg/ml) were added if low yield was expected (<1 ug). The solutions were mixed by inversion (50 times) followed by centrifugation for 10 mins at 4° C. The DNA pellet was washed with 70% EtOH, air-dried and dissolved in 20 ul H₂O. To dissolve the pellet it was incubated for 1 hr at 65° C. with constant vortexing.

TABLE 4 Summary of Discovery Cohort—A list of patient samples used in the discovery of DMRs associated with breast cancer. Sample Code Sample Type ER PR HER2 Grade AGE Year DOB LN Status Status/Outcome 17420*** normal NEG NEG NEG 3 47 2008 Aug. 31, 1961 Neg Unknown 17420*** tumour NEG NEG NEG 47 2008 Aug. 31, 1961 Neg Alive 16693*** normal NEG NEG NEG 3 54 2008 Dec. 6, 1954 Neg Unknown 16693*** tumour NEG NEG NEG 54 2008 Dec. 6, 1954 Neg Alive 23622*** normal NEG NEG NEG 3 68 2009 Sep. 22, 1941 Neg Unknown 23622*** tumour NEG NEG NEG 68 2009 Sep. 22, 1941 Neg Unknown 20578*** normal NEG NEG NEG 3 Unknown 2008 Unknown Pos Unknown 20578*** tumour FF NEG NEG NEG 3 Unknown 2008 Unknown Pos Unknown 20578*** tumour NEG NEG NEG Unknown 2008 Unknown Pos Unknown  2850*** normal NEG NEG NEG 3 30 1992 Sep. 7, 1962 Pos Alive  2850*** tumour NEG NEG NEG 30 1992 Sep. 7, 1962 Pos 14906*** normal NEG NEG NEG 3 48 2002 Jan. 23, 1954 Pos Alive 14906*** tumour NEG NEG NEG 3 48 2002 Jan. 23, 1954 Pos  4537 tumour NEG NEG NEG 3 73 2002 Jun. 27, 1929 Neg Alive  7700 tumour NEG NEG NEG 3 40 2002 Dec. 13, 1962 Neg Alive 19038 tumour NEG NEG NEG 3 33 1999 Jul. 14, 1966 Neg Alive  2319 tumour NEG NEG NEG 3 60 1998 Sep. 15, 1938 Neg Alive  8948 tumour NEG NEG NEG 3 48 2003 Mar. 21, 1955 Neg Alive 19328 tumour NEG NEG NEG 3 27 1999 Nov. 28, 1972 Neg Alive 12627 tumour NEG NEG NEG 3 50 1996 Nov. 2, 1946 Pos Feb. 28, 1999  2532 tumour NEG NEG NEG 3 Unknown unknown Apr. 11, 1935 Pos Sep. 4, 1999  1138 tumour NEG NEG NEG 3 62 1997 Mar. 19, 1935 Pos Alive  214 tumour NEG NEG NEG 3 66 2003 Mar. 31, 1937 Pos Alive  3050 tumour NEG NEG NEG 3 48 2002 Sep. 16, 1954 Pos Alive  4488 tumour NEG NEG NEG 3 unknown unknown May 31, 1927 Unknown Aug. 16, 1996  2032 tumour NEG NEG NEG 39 2002 Dec. 11, 1963 Neg Alive ***denotes paired normal/tumour samples where the sample code is the same

1.1.3 Enrichment of Methylated DNA by MBDCap

The MethylMiner™ Methylated DNA Enrichment Kit (Invitrogen) was used to isolate methylated DNA. 500 ng-1 μg of the genomic DNA previously isolated from tissue samples was sonicated to 100-500 bp. MBD-Biotin Protein (3.5 μg) was coupled to 10 μl of Dynabeads M-280 Streptavidin according to the manufacturer's instructions. The MBD-magnetic bead conjugates were washed three times and resuspended in 1 volume of 1× Bind/Wash buffer. The capture reaction was performed by the addition of 500 ng-1 μg sonicated DNA to the MBD-magnetic beads on a rotating mixer for 1 h at room temperature. All capture reactions were done in duplicate. The beads were washed three times with 1× Bind/Wash buffer. The bound methylated DNA was eluted as a single fraction with a single High Salt Elution Buffer (2,000 mM NaCl). Each fraction was concentrated by ethanol precipitation using 1 μl glycogen (20 μg/μl), 1/10 volume of 3 M sodium acetate, pH 5.2 and 2 sample volumes of 100% ethanol and resuspended in 60 μl H2O.

Enrichment of methylated DNA after capture was assessed by quantitative PCR of control genes of known methylation status; namely EN1 (heavily methylated) and GAPDH (unmethylated) (Perou et al., (2000) Nature, 406:747-752) and both showed enrichment (FIG. 4). In particular, FIG. 4(A) summarises the number of functional/regulatory elements overlapping CpG sites covered by the MBDCap-Seq and HM450K platforms. For each platform two sets of numbers are shown with the second set (>=3 CpGs column) corresponding to elements overlapping 3 or more covered CpG sites. In both cases the fraction of covered elements is given in parentheses. Computational analysis of SssI MBDCap-Seq revealed that MBDCap-Seq can robustly assess the methylation status of 230,655 regions spanning a total of 1.1.6 Mbp, comprising 5,012,633 CpG dinucleotides, or approximately 18% of the total number of CpG sites in the human genome. The assayed CpG sites span 91% of all CpG islands; 84% CpG island shores; 72% RefSeq promoters; 38% introns and 31% exons. To assess CpG methylation coverage of additional functional elements in human mammary epithelial cells (HMEC), computationally derived annotation were used (Ernst et al., (2011) Nature, 473:43-49) to determine the extent of interrogation of HMEC promoters, enhancers and insulators. As a result, it was found that MBDCap-Seq interrogates approximately 66% HMEC promoters; 14% HMEC enhancers, and 12% HMEC insulators. Next, coverage of MBDCap-Seq was compared with coverage of the Illumina HumanMethylation450K (HM450K) array, and it was found that MBDCap-Seq interrogates an additional 4,740,327 CpG sites as compared to the high-density HM450K array. A major advantage of the MBDCap-Seq method is the ability to interrogate regional blocks of hypermethylation, that is methylation spanning consecutive CpG sites, which commonly occurs in cancer. Regional MBDCap-Seq coverage was therefore compared to coverage of HM450K arrays, by requiring that each functional element is covered by 3 or more assayed CpG sites. Interestingly, while MBDCap-Seq and HM450K arrays have similar regional coverage of CpG islands (91% vs. 81%) and RefSeq promoters (71% vs. 83%), MBDCap-Seq regional coverage of shores (77% vs. 28%), enhancers (12% vs. 2%) and insulators (11% vs. 1%) is much greater, highlighting the potential advantage of MBDCap-Seq in screening novel functional regions of the cancer methylome. FIG. 4(B) illustrates the overlap between CpG sites covered by MBDCap-Seq and HM450K platforms; for MBDCap-Seq covered CpGs are those overlapping SssI regions.

1.1.4 Preparation of MBDCap-Seq Libraries and Illumina Sequencing

To enable comparison of genome-wide analysis for the affinity captured methylated DNA from FF and FFPET DNA, libraries were prepared and Illumina sequencing performed.

10 ng DNA of MBDCap enriched DNA was prepared for Ilumina sequencing using the Illumina ChIP-Seq DNA sample prep kit (IP-102-1001) according to the manufacturer's instructions. The library preparation was analyzed on Agilent High Sensitivity DNA 1000 Chip. Each sample was sequenced on one lane of the GA11x.

1.1.5 Sequenom Quantitative MassARRAY Methylation Analysis

Sequenom MassARRAY methylation analysis was performed as described previously in Perou CM: (2011) Oncologist, 16 Suppl 1:61-70. 500 ng of FFPET clinical sample DNA was extracted and bisulphite treated using the standard bisulphite protocol (Blows et al., (2010) PLoS Med., 7:e1000279). As controls for the methylation analysis, whole genome amplified (WGA) DNA (0% methylated) and M.SssI treated DNA (100% methylated) were bisulphite treated in parallel. The primers were designed using the EpiDesignerBETA software from Sequenom (See Tables 5A-5C). Each reverse primer has a T7-promoter tag (5-CAG TAA TAC GAC TCA CTA TAG GGA GAA GGC T-3) and each forward primer has a 10-mer tag (5-AGG AAG AGA G-3). Upon testing these primers on bisulphite treated DNA, all the primers gave specific PCR products at a Tm of 60° C. In order to check for potential PCR bias towards methylated or non-methylated sequences, serological DNA (Millipore) was used as a 100% methylated control and Whole Genome Amplified human blood DNA used as a 0% methylated control. The PCRs were optimized and performed in triplicate using the conditions: 95° C. for 2 min, 45 cycles of 95° C. for 40 sec, 60° C. for 1 min and 72° C. for 1 min 30 sec and final extension at 72° C. for 5 min. After PCR amplification, the triplicates were pooled and a Shrimp Alkaline Phosphatase (SAP) treatment was performed using 5 μl of the PCR product as template. 2 μl of the SAP-treated PCR product was taken and subjected to in vitro transcription and RNaseA Cleavage for the T-cleavage reaction. The samples were purified by resin treatment and spotted on a 384-well SpectroCHIP by a MassARRAY Nanodispenser. This was followed by spectral acquisition on a MassARRAY Analyser Compact matrix-assisted laser desorption/ionisation time-of-flight mass spectrometry. The results were then analysed by the EpiTYPER software V 1.0 which gives quantitative methylation levels for individual CpG sites. The average methylation ratio was calculated by averaging the ratios obtained from each CpG site.

For the Sequenom validation, sample sizes were determined for a two sample t-test with a 2-sided alpha of 0.01, assuming 5 regions were to be investigated. Assuming the difference in average methylation levels is 0.25 (tumors: SD=0.2, normals: SD=0.05), in order to have 90% power to establish a significant difference between tumor and normal samples, 15 samples per group were required.

TABLE 5(A)-(C) Sequenom MassARRAY methylation analysis: A list of primers used for technical and independent validations with Sequenom. Primer Name Position Primer Sequence 5′-3′ SEQ ID NO (A) Sequenom primers for validation of MBDCap.Seq data GHSRseq_01F chr3: 173648423-173648622 aggaagagagGTTTTTTGGTGATTATTATTT  1 TGGT GHSRseq_01R cagtaatacgactcactatagggagaaggct  2 TTCCTCTACATACCCCTAAACCTC GHSRseq_02F Cbr3: 173648692-173648846 aggaagagagGATATTATTAGTATGGTGAGT  3 AGGTTGTTA GHSRseq_02R cagtaatacgactcactatagggagaaggct  4 ACCTAAACTAAAATACTTCCCCC PLD5seq_01F chr1: 240753468-240753604 aggaagagagTGGGTTTAAGTAGAGAGGATT  5 ATTAATTG PLD5seq_02R cagtaatacgactcactatagggagaaggct  6 CTATTTTACTCAATCCAAAAACTAACAAAT PLD5seq_02F chr1: 240753604-240751733 aggaagagagTGGAGGTGTTTGTGTAGAGTG  7 AGTA PLD5seq_02R cagtaatacgactcactatagggagaaggct  8 ACTAAATAAAACAACCCCCAAAAAC PLD5seq_03F chr1: 240751712-240751827 aggaagagagGGAGGGGAGGGTAGGTTTTTT  9 PLD5seq_03R cagtaatacgactcactatagggagaaggct 10 CACTCTACACAAACACCTCCAAAAC CpG 75seq_01F chr1: 147404239-147404367 aggaagagagGTTTTTTTATTTTTTGGGATG 11 TTATAGA CpG 75seq_01R cagtaatacgactcactatagggagaaggct 12 ACCCCACTAAAACTACTTTCCCTAC CpG 75seq_02F chr1: 147404516-147404624 aggaagagagTTTAAGTGAGTTTGATGTTTT 13 TGGG CpG 75seq_02R cagtaatacgactcactatagggagaaggct 14 ATAAACCTTAACTTCCCATCCAAAC CpG 245seq_01F chr8: 54015671-54015794 aggaagagagGTTAGGATTGTTATTATTAGG 15 AAGGTT CpG 245seq_01R cagtaatacgactcactatagggagaaggct 16 CACCATCTATATCCTCTATACCACCC CpG 245seq_02F chr8: 54015238-54015397 aggaagagagAGTAGAGGTTGGAGAAGGTGT 17 TGTAT CpG 245seq_02R cagtaatacgactcactatagggagaaggct 18 ACCTATTCATCCTCAACCTAACCAT CpG 109seq_01F chr9: 103288127-103288472 aggaagagagTTAGGGTTTTGGATTTAGTGT 19 TTGT CpG 109seq_01R cagtaatacgactcactatagggagaaggct 20 TCCCTCCCAAAACCAAAAA CpG 109seq_02F chr9: 101288784-101288140 aggaagagagGTAGTTGGGGGTTGGGGAG 21 CpG 109seq_02R cagtaatacgactcactatagggagaaggct 22 AACCCCTACTACCCTAAAAACCC CpG 100seq_01F chr16: 84489852-84490026 aggaagagagTTGTGGATTTTGATTAATGGG 23 T CpG 100seq_01R cagtaatacgactcactatagggagaaggct 24 AAAAACTTTCCCAAAAATTCC CpG 100seq_02F chr16: 84489673-84489812 aggaagagagGTTGGATATTGGGTAGTGATG 25 TT CpG 100seq_02R cagtaatacgactcactatagggagaaggct 26 AACCTCCTTTAAAACTAAAATTCTCC Cpg 261seq_01F chr6: 337531-337687 aggaagagagTTGGGTAGGGATAGGGGTTTT 27 ATT Cpg 261seq_01R cagtaatacgactcactatagggagaaggct 28 CTCTATCAATTTCCACTCCCCTAAC CpG 24seq_01F chr1: 198276357-198276524 aggaagagagGTTAGGGAGGGAATAATGTTA 29 GGAA CpG 24seq_01R cagtaatacgactcactatagggagaaggct 30 ACCCTAAACTCTACTCTCAACACCTC CpG 24seq_02F chr1: 198276693-198276861 aggaagagagGGTTGTGAGAGTTTTTTAGAG 31 TTGAA CpG 24seq_02R cagtaatacgactcactatagggagaaggct 32 ACTAAAATCCCCCAACCCAAC CpG 188seq_01F chr11: 32412596-32412728 aggaagagagAGGTTTTTAGTTTTGTTTGGT 33 TTTG CpG 188seq_01R cagtaatacgactcactatagggagaaggct 34 AACTCTTACCCAACTACTTCCCAA CpG 272seq_01F chr20: 61107904-61108031 aggaagagagTATTTGTTAGGGTAGGGTTTT 35 AGGG CpG 272seq_01R cagtaatacgactcactatagggagaaggct 36 TAACCTTCCTCAACCAAAACCAA (B) Sequenom Primers: for N vs T Validation C9orf125_01F chr9: 103288327-103288472 aggaagagagTTAGGGTTTTGGATTTAGTGT 37 TTGT C9orf125_01R cagtaatacgactcactatagggagaaggct 38 TCCCTCCCAAAACCAAAAA NPY_manF chr7: 24291366-24291534 aggaagagagGAGTTTTTTGTGTTTGTAGAT 39 GTTAGG NPY_manR cagtaatacgactcactatagggagaaggct 40 CCRAATAATATCTAACCATATCCTCC HMX2_05F chr10: 124892242-124892377 aggaagagagGTTTTTTGGTTGGGTTATTGA 41 GT HMX2_05R cagtaatacgactcactatagggagaaggct 42 AACACCTAAACTCCCCTTAAAAATC FFRD3L_02F chr7: 19151437-19151604 aggaagagagGGGGAGGTTAGGGATAGGTT 43 FERD3L_02R cagtaatacgactcactatagggagaaggct 44 TACCCCATCAAATTCAAAACTATTA SATB2_01F chr2: 200043840-200043962 aggaagagagGTTTTTGGTTGTAGTTTTTGG 45 GATT SATB2_01R cagtaatacgactcactatagggagaaggct 46 ATAAACAACCTCCCACTTTAAAACTAA (C) Sequenom Primers: for T Technical Validation CNTD2_01F Chr19: 45424298-45424447 aggaagagagATTTTGTAAAGGAGAGGGTTT 47 GG CNTD2_01R cagtaatacgactcactatagggagaaggct 48 TCTCTTCCCCTAACTACAAAAATC RREB1_01F Chr6: 7052019-7052195 aggaagagagGGAGTTTTTTGAGAGAAAGAG 49 ATATTG RRKB1_01R cagtaatacgactcactatagggagaaggct 50 ACACCCCACAAAAAATACTTCAAC

1.1.6 Computational Analysis of MBDCap-Seq Data

1.1.6.1 Alignment

MBDCap-Seq sequenced reads were aligned to the hg18 version of the human genome with BOWTIE v1.0. Reads with more than three mismatches and reads mapping to multiple positions were removed. Finally, multiple reads mapping to exactly the same genomic coordinate were eliminated and only one read was retained for downstream analysis to remove redundancy. Alignment statistics for samples used in this study are provided in Table 6.

TABLE 6 Alignment statistics: for each sample the (i) total number of reads in the library, (ii) number of aligned reads, and (iii) number of unique reads are provided. Sample ID Total reads Aligned reads Unique reads 17420 Normal 41,214,252 21,396,042 16,524,012 17420 Tumor 40,818,983 24,232,321 18,884,159 16693 Normal 41,096,580 20,734,837 15,979,081 16693 Tumor 39,360,609 22,063,794 16,031,831 23622 Normal 38,285,502 20,153,788 16,611,821 23622 Tumor 37,404,722 21,637,499 16,547,504 2032 Tumor 40,053,141 17,688,522 10,537,184 4537 Tumor 40,806,884 19,252,828 13,006,143 7700 Tumor 40,245,430 22,013,746 15,838,961 19038 Tumor 39,025,015 19,543,733 14,041,729 2319 Tumor 36,864,046 18,406,242 13,986,362 8948 Tumor 38,280,170 17,047,988 11,506,247 19328 Tumor 38,497,959 16,647,038 10,707,102 20578 Normal 41,850,776 19,673,402 13,675,555 20578 Tumor FF 12,635,126 7,344,021 6,645,009 20578 Tumor 11,105,400 6,164,585 5,345,885 2850 Normal 39,184,148 15,936,763 11,542,100 2850 Tumor 17,615,232 10,002,313 7,787,875 14906 Normal 41,562,010 16,405,617 9,099,153 14906 Tumor 40,430,984 17,411,629 11,818,921 12627 Tumor 16,283,374 8,432,654 6,159,450 2532 Tumor 37,558,382 17,567,682 13,898,084 1138 Tumor 23,312,671 10,738,212 5,892,046 214 Tumor 22,290,885 10,776,952 7,835,497 3050 Tumor 26,040,883 10,929,464 7,563,843 4488 Tumor 39,937,701 20,672,983 14,388,291 Roche Blood 37,445,060 28,126,429 23,155,761 Roche Blood Sssl 33,374,082 21,324,538 19,131,929

1.1.6.2 Identification of Differentially Methylated Regions (DMRs)

In order to accurately delineate regions of the genome assayable by MBDCap-Seq, a fully methylated sample (SssI blood sample) was used to guide us to the genomic regions attracting sequenced tags. More specifically, the findPeaks peak calling utility from HOMER suite of programs (Jatoi et al., (2011) Journal of Clinical Oncology 29:2301-2304) was applied to the fully methylated sample (with parameter settings of—style histone—size 300—minDist 300—tag Threshold 18) to identify 230,655 regions covering approximately 116 Mbp of the genome. These regions are interchangeably referred to as regions of interest or SssI regions.

For each MBDCap-Seq sample to be analyzed, the number of sequenced tags overlapping SssI regions were computed, which resulted in table of counts where columns are samples and rows are SssI regions. The edgeR Bioconductor package (Park et al., (2011) Annals of Oncology, 22:1554-1560), available at URL http://www.bioconductor.org/packages/release/bioc/html/edgeR.html, was used to model distribution of reads between normal (n=6) and tumor (n=19) group of samples in the discovery cohort. Since the edgeR package does not support modelling of paired and unpaired data simultaneously, two separate analyses were performed, a paired analysis with 6 normal/tumor pairs and unpaired analysis with all the samples, and then intersected the results.

1.1.6.3 Clustering of MBDCap-Seq Data

The number of reads mapping to a particular region of a genome does not depend solely on the average level of methylation in the region, but also on other factors, such as density of methylated CpG nucleotides. In order to compare MBDCap-Seq readout to other more quantitative technologies, such as HM450K and Sequenom, a fully methylated MBDCap-Seq sample was used to normalize MBDCap-Seq readouts for samples in the discovery cohort. More specifically, let X_(i) be the number of tags overlapping region i and N be the total number of tags overlapping SssI regions in the sample to be normalized and Y_(i) and M be the corresponding numbers in the control sample. Then, the normalized number of tags overlapping the region i is given by log(X _(i) /N·M/Y _(i)+1)

The normalized tag counts were used for heatmap visualization in FIG. 1, for comparison to HM450K in FIG. 5 and for comparison to Sequenom in FIG. 7.

1.1.7 Functional Annotations of the Genome

1.1.7.1 CpG Islands and Shores

CpG island annotation for hg18 was obtained from UCSC genome browser. The location of CpG island shores was derived from CpG islands by taking +/−2 Kb flanking regions and removing any overlaps with CpG islands.

1.1.7.2 RefSeq Transcripts

RefSeq transcript annotation for hg18 was obtained from UCSC genome browser. Promoters were defined as +2000/−100 bp around transcription start site (TSS). Intergenic regions were defined as regions complementing transcript regions extended to +/−2 Kb around the transcripts.

1.1.7.3 HMEC ChromHMM

HMEC ChromHMM annotations for hg18 were downloaded from ENCODE. The original annotation partitions the HMEC genome into 15 functional states (see FIG. 1b in Reis-Filho and Pusztai, (2011) The Lancet, 378:1812-1823). For the sake of brevity, the three original promoter states were collapsed into one promoter state and the four original enhancer states collapsed into one enhancer state (FIG. 1C and FIG. 4B).

1.1.8 Acquisition and Analysis of TCGA Data

1.1.8.1 Acquisition of TCGA data

Several molecular datasets from TCGA breast cancer (BRCA) cohort were used throughout the study e.g., for validation. Clinical annotation of samples was obtained from the TCGA publication in TCGA (2012) Nature, 490:61-70 (Supplementary Table 1). Raw HM450K methylation data (Level 1) was obtained from TCGA data portal in January 2012. Methylation data spanned 67 normal and 354 tumor ER+ve samples, 16 normal and 105 tumor ER−ve samples, and 9 normal and 73 tumor TNBC samples.

Processed array expression data (Level 3) was obtained from TCGA data portal in March 2012. Expression data spanned 52 normal and 406 tumor ER+ve samples, 9 normal and 118 tumor ER−ve samples, and 8 normal and 89 tumor TNBC samples. Processed RNA-Seq expression data (Level 3) was obtained from TCGA data portal in December 2012. Expression data spanned 73 normal and 588 tumor ER+ve samples, 19 normal and 174 tumor ER−ve samples, and 12 normal and 119 tumor TNBC samples. TCGA BRCA mutation data was obtained from COSMIC database (http://cancer.sanger.ac.uk/cosmic/study/overview?study_id=414). Genes mutated in 2 or more patients were declared as recurrently mutated.

1.1.8.2 Analysis of HM450K Methylation Data

The raw data was pre-processed and background normalized with Bioconductor minfi package using preprocess Illumina ( . . . , bg.correct=TRUE, normalize=“controls”, reference=1) command; resulting M-Values were used for statistical analyses (Laird P W (2003) Nat. Rev. Cancer, 3:253-266). and Beta-Values for heatmap visualizations and clustering. To identify TNBC specific HM450K probes, a t-test comparison between TNBC (n=73) and non-TNBC (n=386) tumors was carried out. This analysis resulted in 282 probes having adj. p-value less than 0.05 and estimated mean difference of methylation between TNBC and non-TNBC tumors of at least 10%; these probes were declared as TNBC specific. Regions overlapping 3 or more TNBC specific probes were declared as TNBC specific.

1.1.5.3 Analysis of Array Expression Data

Differential expression analysis between normal (n=8) and tumor (n=89) TNBC samples was carried out with Bioconductor limma package. Since only subset of tumor samples had paired adjacent normal samples, patient data was treated as random effect using limma's duplicateCorrelation( . . . ) function. This analysis resulted in 3,017 down-regulated and 3,407 up-regulated genes with adj. p-value less than 0.05 out of 17,655 genes on the array. When considering genes with SssI regions in their promoter regions only, these numbers of genes on the array was reduced to 15,543, out of which 2,119 were down-regulated and 2,722 were up-regulated.

1.1.8.4 Analysis of RNA-Seq Expression Data

Log transformed RNA-Seq expression values were used to highlight relationship between methylation and expression for a number of candidate regions in FIG. 2C and FIG. 11.

1.2 Results

1.2.1 Genome Coverage of MBDCap-Seq

Computational analysis of SssI MBDCap-Seq revealed that MBDCap-Seq can robustly assess the methylation status of 230,655 regions spanning a total of 116 Mbp, comprising 5,012,633 CpG dinucleotides, or approximately 18% of the total number of CpG sites in the human genome (FIG. 4A). The assayed CpG sites span 91% of all CpG islands; 84% CpG island shores; 72% RefSeq promoters; 38% introns and 31% exons. Furthermore, a comparison of MBDCap-Seq with the Illumina HumanMethylation450K (HM450K) array (FIG. 4B) revealed that MBDCap-Seq is able to interrogate a further 4,740,327 CpG sites relative to the high-density HM450K array.

A major advantage of the MBDCap-Seq method is its ability to interrogate regional blocks of hypermethylation i.e., methylation spanning consecutive CpG sites, which commonly occurs in cancer. In this respect, comparison of regional MBDCap-Seq coverage to that of HM450K arrays (Supplementary FIG. S1A) showed that while MBDCap-Seq and HM450K arrays have similar regional coverage of CpG islands (91% vs. 81%) and RefSeq promoters (71% vs. 83%), MBDCap-Seq regional coverage of shores (77% vs. 28%), enhancers (12% vs. 2%) and insulators (11% vs. 1%) is much greater, highlighting the potential advantage of MBDCap-Seq in screening novel functional regions of the cancer methylome.

1.2.2 Accuracy of Methylation Analysis Using DNA from FFPE Tissue

To determine if MBDCap-Seq can provide accurate methylation analysis for DNA from FFPET, DNA methylation profiles from DNA isolated from FF and FFPET of matching tumor and lymph node samples were compared.

MBDCap-Seq from FFPET was shown to provide equivalent methylation to FF DNA (Pearson Correlation Coefficient of 0.95 and 0.86, respectively) (FIG. 5A). Furthermore, analyses using MBDCap-Seq and the HM450K array performed on the same FFPET tumor and lymph node DNA were shown to have high concordance (0.79 & 0.77 respectively) (FIGS. 5B-D).

1.2.3 Identification and Validation of Differentially Methylated Regions in TNBCs

To identify differentially methylated regions (DMRs) in Triple Negative Breast Cancers (TNBCs), methylation profiles were determined for FFPET DNA from a discovery cohort of 19 Grade 3 TNBCs tumor and 6 matched normal samples (Table 4) using MBDCap-Seq according to the methods discussed previously. The data obtained was analysed using a novel computational pipeline for comparative statistical analysis of MBDCap-Seq samples as discussed previously (FIG. 6).

822 hypermethylated and 43 hypomethylated statistically significant DMRs (FDR<0.05) were identified, harboring 64,005 and 623 CpG sites respectively, compared to matched normal samples (FIGS. 1A-B and Table 1) and validated sample-specific differential methylation using Sequenom DNA methylation analysis (FIG. 7).

Of the DMRs identified, it was found that CpG islands, CpG island shores and promoters are significantly over-represented in the 822 hypermethylated regions and under-represented in the 43 regions of hypomethylation (FIG. 1C). Notably, ChromHMM annotated HMEC promoters and polycomb repressed regions were also significantly enriched for gain of methylation in the breast cancer samples. A subset of the DMRs were validated in an independent cohort of 31 TNBCs (Table 7), a panel of cell lines (Table 8), and 15 normal breast samples (Table 9), using Sequenom.

TABLE 7 Clinical samples from independent cohort of 31 TNBCs used for validation of selected DMRs with Sequenom Lymph Tumour Patient ER Pr HER2 node Age at size No. Grade status status status status diagnosis (mm) 08P18585 3 — — — 0/1 36 35 08P19084 3 — — — 0/1 44 24 07P09975 2 & 3 — — — 0/4 45 30 09P05790 3 — — — 0/4 48 36 09P07178 3 — — —  0/19 50 60 07P19869 3 — — — 0/3 51 46 09P14372 3 — — — 0/2 52 25 09P01080 3 — — —  0/23 52 25 09P13385 2 — — — No lymph 57 23 nodes found 07P18351 3 — — —  0/12 61 36 07P09663 3 — — —  0/15 69 35 07P10215 1 — — — 0/1 73 16 09P13015 3 — — — 0/1 73 40 08P16834 2 — — — 0/8 80 20 09P15105 3 — — — 0/4 84 43 07P18017 3 — — —  1/17 39 28 09P14783 3 — — —  1/15 42 25 08P20420 3 — — = 1/9 42 33 09P06626 3 — — = 1/1 45 20 09P15999 3 — — —  4/13 47 30 09P09950 3 — — — 1/3 48 15 09P07430 3 — — — 1/2 50 12 08P20600 3 — — — 2/4 50 45 09P04704 3 — — =  1/17 55 30 04P15487 3 — — — 23/25 55 28 08P15189 2 — — —  9/22 56 100 08P10459 3 — — — 1/3 64 15 04P7410 3 — — —  1/23 66 40 08P20503 3 — — —  2/17 72 17 09P03391 3 — — — 16/19 78 35 07P13965 3 — — —  2/18 78 38

TABLE 8 Cell lines used for validation of selected DMRs with Sequenom Breast Cell lines Classification Tumour cell lines HBL-100 Basal B MDA-MB-330 unclassified BT-20 Basal A HCC-1187 Basal A HCC-1569 Basal A HCC-1937 Basal A HCC-1954 Basal A HCC-70 Basal A MDA-MB-468 Basal A BT-549 Basal B HCC-1500 Basal B HCC-38 Basal B Hs578.T Basal B MCF-12A Basal B MDA-MB-157 Basal B MDA-MB-231 Basal B MDA-MB-436 Basal B BT-483 Luminal MCF-7 Luminal MDA-MB-175 Luminal MDA-MB-361 Luminal SK-Br3 Luminal T-47D Luminal ZR-75-1 Luminal Normal cell lines 184 A1 Normal 184 B5 Normal HMEC 219-4 Normal

TABLE 9 Clinical samples from independent cohort of 14 non-cancer patients (normal) used for validation of selected DMRs with Sequenom SampleID Sample Type Bre12 Normal Bre13 Normal Bre67 Normal Bre71 Normal Bre76 Normal Bre78 Normal Bre85 Normal Bre88 Normal Bre97 Normal Bre98 Normal Bre101 Normal Bre103 Normal Bre113 Normal Bre117 Normal

Sequenom methylation analysis performed on 5 of the 822 hypermethylated regions spanning the CpG island promoters of NPY, FERD3L, HMX2, SATB2 and C9orf125 showed that the levels of methylation in the normal samples were uniformly low, whereas the 5 DMRs showed striking hypermethylation in the TNBCs (FIG. 10), and 24 breast cancer cell lines (FIG. 1E).

1.2.4 Functional Characterization of Genes With Promoter Hypermethylation

To predict the potential functional significance of the 822 DMRs identified in the TNBC, those regions which overlapped with promoters and genes were determined. It was found that the 822 DMRs were associated with 513 RefSeq promoters, which corresponded to 308 genes. These genes can be identified using nucleotide analysis software available in the art.

TABLE 10 A list of RefSeq transcripts whose promoters overlap one or more hypermethylated DMRs. The following information is listed for each transcript: (i) promoter genomic coordinates with respect to hg18 (−2000/+100 base pairs around transcript TSS), (ii) region id of the overlapping DMR, (iii) official gene symbol, and (iv) transcript type. The table contains 513 unique transcripts which correspond to 308 unique gene symbols. Start End Transcript Chromosome (promoter) (promoter) Transcript ID Strand Region ID Gene name type 1 2,973,602 2,975,701 NM_199454 + chr1-38247 PRDM16 1 1 2,973,602 2,975,701 NM_022114 + chr1-38247 PRDM16 1 1 75,371,155 75,373,254 NM_001256114 + chr1-53988 NA NA 1 85,298,581 85,300,680 NM_145172 + chr1-49613 WDR63 1 1 147,192,910 147,195,009 NR_027356 + chr1-11916 8 1 147,192,910 147,195,009 NR_027355 + chr1-11916 8 1 147,195,029 147,197,128 NR_027354 + chr1-11916 2 1 151,915,788 151,917,887 NM_000906 + chr1-20728 NPR1 1 1 168,897,937 168,900,036 NM_006902 + chr1-48261 PRRX1 1 1 168,897,937 168,900,036 NM_022716 + chr1-48261 PRRX1 1 1 177,976,921 177,979,020 NM_173509 + chr1-11343 FAM163A 1 1 179,717,309 179,719,408 NM_001205294 + chr1-48028 CACNAIE 1 1 179,717,309 179,719,408 NM_001205293 + chr1-48028 CACNAIE 1 1 179,717,309 179,719,408 NM_000721 + chr1-48028 CACNAIE 1 1 222,868,802 222,870,901 NM_152495 + chr1-4629 CNIH3 1 1 235,270,325 235,272,424 NM_001035 + chr1-39636 RYR2 1 1 235,270,325 235,272,424 NM_001035 + chr1-32204 RYR2 1 1 242,145,327 242,147,426 NR_033883 + chr1-32857 3 1 246,085,124 246,087,223 NM_015431 + chr1-38532 TRIM58 1 1 2,974,050 2,976,149 NR_015440 − chr1-38247 3 1 2,974,050 2,976,149 NR_024371 − chr1-38247 3 1 37,272,332 37,274,431 NM_000831 − chr1-29954 GRIK3 1 1 38,003,312 38,005,411 NM_001099439 − chr1-6836 EPHA10 1 1 38,003,312 38,005,411 NM_173641 − chr1-6836 EPHA10 1 1 50,661,608 50,663,707 NM_032110 − chr1-29436 DMRTA2 1 1 58,488,700 58,490,799 NM_021080 − chr1-23869 DAB1 1 1 63,555,390 63,557,489 NR_038252 − chr1-38653 LINC00466 3 1 90,955,283 90,957,382 NM_020063 − chr1-20354 BARHL2 1 1 103,346,541 103,348,640 NM_080629 − chr1-4856 COL11A1 1 1 103,346,541 103,348,640 NM_001190709 − chr1-4856 COL11A1 1 1 103,346,541 103,348,640 NM_080630 − chr1-4856 COL11A1 1 1 103,346,541 103,348,640 NM_001854 − chr1-4856 COL11A1 1 1 152,741,051 152,743,150 NM_001010846 − chr1-27293 SHE 1 1 154,656,746 154,658,845 NR_029691 − chr1-41132 MIR9-1 4 1 167,663,195 167,665,294 NM_021179 − chr1-40928 Clorf114 1 1 210,939,851 210,941,950 NM_018664 − chr1-28592 BATF3 1 1 227,636,367 227,638,466 NM_001100 − chr1-48080 ACTA1 1 1 240,754,522 240,756,621 NM_152666 − chr1-24072 PLD5 1 10 6,282,846 6,284,945 NM_004566 + chr10-2387 PFKFB3 1 10 22,672,380 22,674,479 NM_012443 + chr10-6953 SPAG6 1 10 22,672,380 22,674,479 NM_172242 + chr10-6953 SPAG6 1 10 22,672,380 22,674,479 NM_001253854 + chr10-6953 SPAG6 1 10 22,672,380 22,674,479 NM_001253855 + chr10-6953 SPAG6 1 10 23,519,466 23,521,565 NM_178161 + chr10-9290 PTF1A 1 10 25,502,296 25,504,395 NM_020752 + chr10-32739 GPR158 1 10 26,543,242 26,545,341 NM_001134366 + chr10-8209 GAD2 1 10 26,543,242 26,545,341 NM_000818 + chr10-8209 GAD2 1 10 50,486,353 50,488,452 NM_003055 + chr10-35571 SLC18A3 1 10 50,489,160 50,491,259 NM_020985 + chr10-35571 CHAT 1 10 50,489,160 50,491,259 NM_020986 + chr10-35571 CHAT 1 10 50,490,089 50,492,188 NM_001142934 + chr10-35571 CHAT 1 10 50,490,089 50,492,188 NM_020549 + chr10-35571 CHAT 1 10 50,490,089 50,492,188 NM_001142929 + chr10-35571 CHAT 1 10 50,490,089 50,492,188 NM_001142933 + chr10-35571 CHAT 1 10 50,555,690 50,557,789 NM_001042427 + chr10-20530 C10orf53 1 10 50,555,690 50,557,789 NM_182554 + chr10-20530 C10orf53 1 10 101,280,680 101,282,779 NM_145285 + chr10-27233 NKX2-3 1 10 106,388,849 106,390,948 NM_014978 + chr10-3695 SORCS3 1 10 106,388,849 106,390,948 NM_014978 + chr10-22036 SORCS3 1 10 124,895,628 124,897,727 NM_005519 + chr10-2952 HMX2 1 10 134,899,398 134,901,497 NM_014468 + chr10-29209 VENTX 1 10 1,769,571 1,771,670 NM_018702 − chr10-15047 ADARB2 1 10 15,801,677 15,803,776 NM_003638 − chr10-20648 ITGA8 1 10 25,505,112 25,507,211 NR_027333 − chr10-32739 3 10 71,003,117 71,005,216 NM_020999 − chr10-2772 NEUROG3 1 10 118,021,710 118,023,809 NM_145793 − chr10-22147 GFRA1 1 10 118,022,687 118,024,786 NM_001145453 − chr10-22147 GFRA1 1 10 118,023,017 118,025,116 NM_005264 − chr10-22147 GFRA1 1 10 118,887,703 118,889,802 NM_199131 − chr10-19113 VAX1 1 10 118,887,703 118,889,802 NM_001112704 − chr10-19113 VAX1 1 10 132,999,875 133,001,974 NM_174937 − chr10-18957 TCERG1L 1 10 134,449,428 134,451,527 NM_177400 − chr10-17539 NKX6-2 1 11 6,902,230 6,904,329 NM_013250 + chr11-12951 ZNF215 1 11 17,695,686 17,697,785 NM_002478 + chr11-5813 MYOD1 1 11 20,645,693 20,647,792 NM_201551 + chr11-13529 NELL1 1 11 20,645,693 20,647,792 NM_201551 + chr11-30051 NELL1 1 11 20,645,693 20,647,792 NM_006157 + chr11-13529 NELL1 1 11 20,645,693 20,647,792 NM_006157 + chr11-30051 NELL1 1 11 32,411,861 32,413,960 NR_023920 + chr11-24196 WT1-AS 5 11 32,411,861 32,413,960 NR_023920 + chr11-4047 WT1-AS 5 11 46,337,721 46,339,820 NM_001105540 + chr11-6782 DGKZ 1 11 108,796,056 108,798,155 NM_207645 + chr11-28585 C11orf87 1 11 113,433,641 113,435,740 NM_006006 + chr11-7178 ZBTB16 1 11 113,433,641 113,435,740 NM_006006 + chr11-24352 ZBTB16 1 11 113,434,498 113,436,597 NM_001018011 + chr11-7178 ZBTB16 1 11 113,434,498 113,436,597 NM_001018011 + chr11-24352 ZBTB16 1 11 124,238,515 124,240,614 NM_022370 + chr11-25281 ROBO3 1 11 125,277,482 125,279,581 NM_013264 + chr11-24690 DDX25 1 11 131,283,922 131,286,021 NM_001144059 + chr11-31615 NTM 1 11 131,283,922 131,286,021 NM_016522 + chr11-31615 NTM 1 11 131,283,922 131,286,021 NM_001144058 + chr11-31615 NTM 1 11 133,442,030 133,444,129 NM_001205329 + chr11-10966 JAM3 1 11 133,442,030 133,444,129 NM_032801 + chr11-10966 JAM3 1 11 8,147,067 8,149,166 NM_001206672 − chr11-27538 RIC3 1 11 8,147,067 8,149,166 NM_001206671 − chr11-27538 RIC3 1 11 8,147,067 8,149,166 NM_024557 − chr11-27538 RIC3 1 11 8,147,067 8,149,166 NM_001135109 − chr11-27538 RIC3 1 11 8,147,067 8,149,166 NR_045405 − chr11-27538 NA NA 11 19,692,695 19,694,794 NR_015384 − chr11-23818 NA NA 11 20,138,347 20,140,446 NM_001029865 − chr11-13605 DBX1 1 11 27,697,771 27,699,870 NM_001143807 − chr11-3267 BDNF 1 11 27,698,803 27,700,902 NM_001143806 − chr11-3267 BDNF 1 11 27,698,803 27,700,902 NM_001143805 − chr11-3267 BDNF 1 11 27,698,803 27,700,902 NM_170732 − chr11-3267 BDNF 1 11 27,700,082 27,702,181 NM_170731 − chr11-3267 BDNF 1 11 32,408,840 32,410,939 NM_001198551 − chr11-24196 WT1 1 11 32,408,840 32,410,939 NM_001198552 − chr11-24196 WT1 1 11 32,413,558 32,415,657 NM_024424 − chr11-4047 WT1 1 11 32,413,558 32,415,657 NM_024426 − chr11-4047 WT1 1 11 32,413,558 32,415,657 NM_000378 − chr11-4047 WT1 1 11 62,315,963 62,318,062 NM_001080501 − chr11-22903 TMEM223 1 11 74,119,735 74,121,834 NM_015424 − chr11-24335 CHRDL2 1 11 77,411,869 77,413,968 NM_023930 − chr11-27245 KCTD14 1 11 125,278,227 125,280,326 NM_031307 − chr11-24690 PUS3 1 11 132,318,148 132,320,247 NM_002545 − chr11-30995 OPCML 1 11 133,331,991 133,334,090 NM_014987 − chr11-24671 IGSF9B 1 12 4,251,163 4,253,262 NM_001759 + chr12-28277 CCND2 1 12 30,837,882 30,839,981 NR_040245 + chr12-2820 3 12 46,861,633 46,863,732 NM_001013635 + chr12-7564 C12orf68 1 12 70,950,796 70,952,895 NM_013381 + chr12-10548 TRHDE 1 12 103,219,640 103,221,739 NM_001008394 + chr12-26855 EID3 1 12 118,513,647 118,515,746 NM_001136534 + chr12-17914 TMEM233 1 12 127,315,901 127,318,000 NM_001136103 + chr12-23224 TMEM132C 1 12 127,315,901 127,318,000 NM_001136103 + chr12-29205 TMEM132C 1 12 45,759,902 45,762,001 NM_181847 − chr12-24630 AMIGO2 1 12 45,759,902 45,762,001 NM_001143668 − chr12-24630 AMIGO2 1 12 70,953,457 70,955,556 NR_026837 − chr12-10548 3 12 70,953,457 70,955,556 NR_026836 − chr12-10548 3 12 73,889,679 73,891,778 NM_139136 − chr12-10547 KCNC2 1 12 73,889,679 73,891,778 NM_153748 − chr12-10547 KCNC2 1 12 73,889,679 73,891,778 NM_139137 − chr12-10547 KCNC2 1 12 97,812,706 97,814,805 NM_001204081 − chr12-18252 ANKS1B 1 12 97,812,706 97,814,805 NM_001204065 − chr12-18252 ANKS1B 1 12 97,812,706 97,814,805 NM_001204080 − chr12-18252 ANKS1B 1 12 97,812,706 97,814,805 NM_001204079 − chr12-18252 ANKS1B 1 12 106,821,579 106,823,678 NR_037629 − chr12-9653 NA NA 12 109,611,230 109,613,329 NM_032369 − chr12-30179 HVCN1 1 12 109,611,901 109,614,000 NM_001040107 − chr12-30179 HVCN1 1 12 112,394,161 112,396,260 NM_022363 − chr12-894 LHX5 1 12 128,954,066 128,956,165 NM_133448 − chr12-2151 TMEM132D 1 13 27,262,780 27,264,879 NM_145657 + chr13-13783 GSX1 1 13 48,690,475 48,692,574 NM_001507 + chr13-9261 MLNR 1 13 57,101,790 57,103,889 NM_001040429 + chr13-10897 PCDH17 1 13 42,464,278 42,466,377 NM_033255 − chr13-8897 EPSTI1 1 13 42,464,278 42,466,377 NM_001002264 − chr13-8897 EPSTI1 1 13 52,320,677 52,322,776 NM_002590 − chr13-5196 PCDH8 1 13 52,320,677 52,322,776 NM_032949 − chr13-5196 PCDH8 1 13 94,162,291 94,164,390 NM_007084 − chr13-9865 SOX21 1 13 107,317,362 107,319,461 NM_001080396 − chr13-9410 FAM155A 1 14 56,347,654 56,349,753 NR_029385 + chr14-13812 5 14 91,857,905 91,860,004 NM_153646 + chr14-12954 SLC24A4 1 14 21,075,078 21,077,177 NM_005407 − chr14-8228 SALL2 1 14 36,058,555 36,060,654 NM_003317 − chr14-10157 NKX2-1 1 14 36,059,082 36,061,181 NM_001079668 − chr14-10157 NKX2-1 1 14 56,346,838 56,348,937 NM_021728 − chr14-13812 OTX2 1 14 60,022,418 60,024,517 NM_174978 − chr14-7997 C14orf39 1 15 46,955,582 46,957,681 NM_014335 + chr15-7466 EIDI 1 15 58,081,713 58,083,812 NM_012182 + chr15-2485 FOXB1 1 15 32,833,882 32,835,981 NM_020660 − chr15-5377 GJD2 1 15 46,725,178 46,727,277 NM_000138 − chr15-15389 FBN1 1 15 73,792,145 73,794,244 NM_001897 − chr15-12559 CSPG4 1 15 87,239,675 87,241,774 NM_178232 − chr15-6733 HAPLN3 1 16 12,900,978 12,903,077 NM_001145205 + chr16-39747 SHISA9 1 16 12,900,978 12,903,077 NM_001145204 + chr16-39747 SHISA9 1 16 22,731,361 22,733,460 NM_006043 + chr16-21410 HS3ST2 1 16 29,981,101 29,983,200 NM_184043 + chr16-39788 ALDOA 1 16 29,981,319 29,983,418 NM_001127617 + chr16-39788 ALDOA 1 16 29,982,495 29,984,594 NM_184041 + chr16-39788 ALDOA 1 16 29,982,495 29,984,594 NM_001243177 + chr16-39788 ALDOA 1 16 66,118,218 66,120,317 NM_024519 + chr16-38440 FAM65A 1 16 66,118,221 66,120,320 NM_001193522 + chr16-38440 FAM65A 1 16 66,119,041 66,121,140 NM_001193523 + chr16-38440 FAM65A 1 16 81,215,900 81,217,999 NM_001220491 + chr16-10222 CDH13 1 16 81,215,900 81,217,999 NM_001220492 + chr16-10222 CDH13 1 16 81,215,900 81,217,999 NM_001220488 + chr16-10222 CDH13 1 16 81,215,900 81,217,999 NM_001257 + chr16-10222 CDH13 1 16 81,215,900 81,217,999 NM_001220490 + chr16-10222 CDH13 1 16 81,215,900 81,217,999 NM_001220489 + chr16-10222 CDH13 1 16 83,726,257 83,728,356 NR_033984 + chr16-31613 NA NA 16 84,875,538 84,877,637 NR_038438 + chr16-15120 NA NA 16 85,099,634 85,101,733 NM_001451 + chr16-19848 FOXF1 1 16 86,192,000 86,194,099 NM_020655 + chr16-31470 JPH3 1 16 19,992,502 19,994,601 NM_001002911 − chr16-10570 GPR139 1 16 31,121,499 31,123,598 NM_145182 − chr16-16067 PYCARD 1 16 31,121,499 31,123,598 NM_013258 − chr16-16067 PYCARD 1 16 31,135,797 31,137,896 NM_152901 − chr16-16055 PYDC1 1 16 47,873,144 47,875,243 NM_004352 − chr16-27356 CBLN1 1 16 60,628,141 60,630,240 NM_001796 − chr16-28483 CDH8 1 17 43,125,629 43,127,728 NM_014726 + chr17-35096 TBKBP1 1 17 53,587,319 53,589,418 NR_002307 + chr17-23838 MSX2P1 6 17 55,580,084 55,582,183 NM_000717 + chr17-9298 CA4 1 17 70,176,851 70,178,950 NM_175738 + chr17-34300 RAB37 1 17 44,161,011 44,163,110 NM_006361 − chr17-17561 HOXB13 1 17 47,591,032 47,593,131 NM_020178 − chr17-32969 CA10 1 17 47,592,061 47,594,160 NM_001082533 − chr17-32969 CA10 1 17 47,592,277 47,594,376 NM_001082534 − chr17-32969 CA10 1 18 2,835,028 2,837,127 NM_032048 + chr18-11204 EMILIN2 1 18 10,442,625 10,444,724 NM_153000 + chr18-8094 APCDD1 1 18 53,168,719 53,170,818 NM_015879 + chr18-11487 ST8SIA3 1 18 65,217,264 65,219,363 NM_152721 + chr18-12432 DOK6 1 18 73,088,996 73,091,095 NM_001480 + chr18-4037 GALR1 1 18 74,839,263 74,841,362 NM_171999 + chr18-7308 SALL3 1 18 5,187,156 5,189,255 NM_001145194 − chr18-1098 C18orf42 1 18 11,138,662 11,140,761 NM_022068 − chr18-13032 PIEZO2 1 18 42,590,938 42,593,037 NM_013305 − chr18-2933 ST8SIA5 1 18 68,685,691 68,687,790 NM_001201465 − chr18-14616 NETO1 1 18 68,685,691 68,687,790 NM_001201465 − chr18-4761 NETO1 1 18 68,685,691 68,687,790 NM_138966 − chr18-14616 NETO1 1 18 68,685,691 68,687,790 NM_138966 − chr18-4761 NETO1 1 18 68,683,815 68,685,914 NM_138999 − chr18-4761 NETO1 1 18 72,336,035 72,338,134 NM_014643 − chr18-5056 ZNF516 1 19 1,399,148 1,401,247 NM_005883 + chr19-43568 APC2 1 19 22,259,092 22,261,191 NM_001242680 + chr19-14446 ZNF729 1 19 24,059,816 24,061,915 NM_203282 + chr19-36571 ZNF254 1 19 34,707,331 34,709,430 NM_001146339 + chr19-49971 VSTM2B 1 19 34,707,331 34,709,430 NM_001146339 + chr19-29086 VSTM2B 1 19 42,732,113 42,734,212 NR_038249 + chr19-26515 NA NA 19 42,732,113 42,734,212 NR_038250 + chr19-26515 NA NA 19 42,732,113 42,734,212 NM_152606 + chr19-26515 ZNF540 1 19 42,997,891 42,999,990 NR_040013 + chr19-6795 NA NA 19 49,145,237 49,147,336 NM_013359 + chr19-8563 ZNF221 1 19 57,562,982 57,565,081 NM_001145434 + chr19-30142 ZNF880 1 19 61,569,280 61,571,379 NR_003127 + chr19-13959 ZNF542 3 19 61,569,302 61,571,401 NR_024057 + chr19-13959 ZNF542 3 19 61,569,302 61,571,401 NR_024056 + chr19-13959 NA NA 19 61,569,302 61,571,401 NR_024055 + chr19-13959 ZNF542 3 19 61,569,302 61,571,401 NR_033418 + chr19-13959 ZNF542 3 19 61,844,339 61,846,438 NM_001193628 + chr19-24064 NA NA 19 62,701,121 62,703,220 NM_198542 + chr19-31732 ZNF773 1 19 62,785,440 62,787,539 NM_001010879 + chr19-11665 ZIK1 1 19 6,542,064 6,544,163 NM_001252 − chr19-46269 CD70 1 19 9,470,180 9,472,279 NM_152476 − chr19-30352 ZNF560 1 19 10,489,027 10,491,126 NM_030760 − chr19-17643 S1PR5 1 19 10,489,569 10,491,668 NM_001166215 − chr19-17643 S1PR5 1 19 11,549,454 11,551,553 NM_001611 − chr19-8158 ACP5 1 19 11,550,493 11,552,592 NM_001111034 − chr19-8158 ACP5 1 19 11,550,702 11,552,801 NM_001111036 − chr19-8158 ACP5 1 19 11,550,702 11,552,801 NM_001111035 − chr19-8158 ACP5 1 19 17,659,909 17,662,008 NM_001080421 − chr19-26183 UNC13A 1 19 19,793,461 19,795,560 NM_001099269 − chr19-28214 ZNF506 1 19 19,793,461 19,795,560 NM_001145404 − chr19-28214 ZNF506 1 19 21,985,486 21,987,585 NM_007153 − chr19-49834 ZNF208 1 19 22,396,889 22,398,988 NM_001098626 − chr19-31045 ZNF98 1 19 34,708,400 34,710,499 NR_040029 − chr19-49971 NA NA 19 34,708,400 34,710,499 NR_040029 − chr19-29086 NA NA 19 36,531,931 36,534,030 NM_020856 − chr19-24060 TSHZ3 1 19 49,644,406 49,646,505 NM_014518 − chr19-10652 ZNF229 1 19 56,789,346 56,791,445 NR_034159 − chr19-31043 NA NA 19 62,912,292 62,914,391 NM_001085384 − chr19-31544 ZNF154 1 19 62,930,708 62,932,807 NM_024833 − chr19-6109 ZNF671 1 19 63,138,453 63,140,552 NM_133460 − chr19-16291 ZNF418 1 19 63,301,443 63,303,542 NM_001145544 − chr19-19145 ZSCAN18 1 19 63,301,443 63,303,542 NM_023926 − chr19-19145 ZSCAN18 1 19 63,301,443 63,303,542 NM_001145543 − chr19-19145 ZSCAN18 1 2 124,497,334 124,499,433 NM_130773 + chr2-4092 CNTNAP5 1 2 176,663,778 176,665,877 NM_000523 + chr2-22578 HOXD13 1 2 176,687,738 176,689,837 NM_002148 + chr2-9342 HOXD10 1 2 278,209 280,308 NM_001002919 − chr2-45413 FAM150B 1 2 176,656,837 176,658,936 NM_001080458 − chr2-9331 EVX2 1 2 182,253,538 182,255,637 NM_002500 − chr2-13825 NEUROD1 1 2 182,253,538 182,255,637 NM_002500 − chr2-17703 NEUROD1 1 2 192,767,790 192,769,889 NM_016192 − chr2-26540 TMEFF2 1 2 200,044,135 200,046,234 NM_015265 − chr2-27090 SATB2 1 2 219,881,874 219,883,973 NM_001199764 − chr2-6335 PTPRN 1 2 219,882,440 219,884,539 NM_002846 − chr2-6335 PTPRN 1 2 219,882,440 219,884,539 NM_001199763 − chr2-6335 PTPRN 1 2 232,959,899 232,961,998 NR_028501 − chr2-45508 3 2 241,408,299 241,410,398 NM_004321 − chr2-31496 KIF1A 1 2 241,408,299 241,410,398 NM_001244008 − chr2-31496 KIF1A 1 20 21,632,297 21,634,396 NM_006192 + chr20-15341 PAX1 1 20 34,601,301 34,603,400 NM_006097 + chr20-17172 MYL9 1 20 34,601,301 34,603,400 NM_181526 + chr20-17172 MYL9 1 20 61,278,297 61,280,396 NR_029670 + chr20-20481 MIR124-3 4 20 61,839,655 61,841,754 NM_020062 + chr20-19704 SLC2A4RG 1 20 62,179,915 62,182,014 NM_000913 + chr20-17950 OPRL1 1 20 62,179,915 62,182,014 NM_182647 + chr20-17950 OPRL1 1 20 5,242,916 5,245,015 NM_144773 − chr20-4787 PROKR2 1 20 22,507,181 22,509,280 NR_001558 − chr20-11329 LINC00261 7 20 22,507,181 22,509,280 NR_001558 − chr20-5927 LINC00261 7 20 25,010,916 25,013,015 NM_001256271 − chr20-4500 NA NA 20 25,010,916 25,013,015 NM_001256272 − chr20-4500 NA NA 20 25,010,916 25,013,015 NR_045948 − chr20-4500 NA NA 20 25,010,916 25,013,015 NR_045951 − chr20-4500 NA NA 20 25,010,916 25,013,015 NM_014588 − chr20-4500 VSX1 1 20 25,010,916 25,013,015 NM_199425 − chr20-4500 VSX1 1 20 44,575,502 44,577,601 NM_199441 − chr20-7936 ZNF334 1 20 44,575,502 44,577,601 NM_018102 − chr20-7936 ZNF334 1 20 54,013,320 54,015,419 NM_080617 − chr20-4778 CBLN4 1 20 61,108,733 61,110,832 NM_080606 − chr20-22989 BHLHE23 1 20 62,181,190 62,183,289 NM_005873 − chr20-17950 RGS19 1 20 62,181,669 62,183,768 NM_001039467 − chr20-17950 RGS19 1 21 33,318,086 33,320,185 NM_005806 + chr21-7896 OLIG2 1 21 36,991,861 36,993,960 NM_009586 + chr21-8209 SIM2 1 21 36,991,861 36,993,960 NM_005069 + chr21-8209 SIM2 1 21 38,210,467 38,212,566 NM_002240 − chr21-5935 KCNJ6 1 21 38,955,389 38,957,488 NM_001243432 − chr21-5864 ERG 1 21 38,954,362 38,956,461 NM_001243428 − chr21-5864 ERG 1 21 38,955,475 38,957,574 NM_004449 − chr21-5864 ERG 1 21 38,955,475 38,957,574 NM_001136154 − chr21-5864 ERG 1 22 25,381,446 25,383,545 NR_033319 + chr22-15317 MIAT 3 22 25,381,446 25,383,545 NR_033321 + chr22-15317 MIAT 3 22 25,381,446 25,383,545 NR_033320 + chr22-15317 MIAT 3 22 25,381,446 25,383,545 NR_003491 + chr22-15317 MIAT 3 22 17,517,697 17,519,796 NM_005315 − chr22-14473 GSC2 1 22 43,783,439 43,785,538 NM_001242450 − chr22-14234 PHF21B 1 22 43,784,146 43,786,245 NM_138415 − chr22-14234 PHF21B 1 22 43,784,374 43,786,473 NM_001135862 − chr22-14234 PHF21B 1 3 211,279 213,378 NM_006614 + chr3-11819 CHL1 1 3 211,279 213,378 NM_001253387 + chr3-11819 CHL1 1 3 212,326 214,425 NR_045572 + chr3-11819 CHL1 3 3 12,303,436 12,305,535 NM_138711 + chr3-19388 PPARG 1 3 37,007,845 37,009,944 NM_000249 + chr3-8813 MLH1 1 3 37,008,272 37,010,371 NM_001167619 + chr3-8813 MLH1 1 3 37,008,272 37,010,371 NM_001167618 + chr3-8813 MLH1 1 3 37,008,272 37,010,371 NM_001167617 + chr3-8813 MLH1 1 3 44,727,139 44,729,238 NM_033210 + chr3-7330 ZNF502 1 3 44,727,139 44,729,238 NM_001134442 + chr3-7330 ZNF502 1 3 44,727,139 44,729,238 NM_001134441 + chr3-7330 ZNF502 1 3 44,727,139 44,729,238 NM_001134440 + chr3-7330 ZNF502 1 3 63,236,954 63,239,053 NM_001130003 + chr3-4075 SYNPR 1 3 113,532,606 113,534,705 NM_005944 + chr3-20990 CD200 1 3 113,532,606 113,534,705 NM_001004196 + chr3-20990 CD200 1 3 123,383,220 123,385,319 NM_000388 + chr3-13141 CASR 1 3 123,383,871 123,385,970 NM_001178065 + chr3-13141 CASR 1 3 132,561,379 132,563,478 NR_002949 + chr3-1810 3 3 132,561,408 132,563,507 NR_027766 + chr3-1810 3 3 148,607,871 148,609,970 NM_003412 + chr3-9862 ZIC1 1 3 149,896,348 149,898,447 NM_000685 + chr3-2993 AGTR1 1 3 149,896,348 149,898,447 NM_004835 + chr3-2993 AGTR1 1 3 149,896,348 149,898,447 NM_031850 + chr3-2993 AGTR1 1 3 149,896,348 149,898,447 NM_009585 + chr3-2993 AGTR1 1 3 169,448,004 169,450,103 NR_021485 + chr3-19953 EGFEM1P 3 3 174,596,938 174,599,037 NM_014932 + chr3-10180 NLGN1 1 3 13,896,520 13,898,619 NM_004625 − chr3-16318 WNT7A 1 3 27,738,690 27,740,789 NM_005442 − chr3-18012 EOMES 1 3 37,009,700 37,011,799 NM_014805 − chr3-8813 EPM2AIP1 1 3 62,334,131 62,336,230 NM_018008 − chr3-11179 FEZF2 1 3 79,899,650 79,901,749 NM_002941 − chr3-9248 ROBO1 1 3 129,689,355 129,691,454 NM_001145662 − chr3-16920 GATA2 1 3 129,689,964 129,692,063 NM_001145661 − chr3-16920 GATA2 1 3 171,786,458 171,788,557 NM_020949 − chr3-22674 SLC7A14 1 3 173,648,841 173,650,940 NM_198407 − chr3-1158 GHSR 1 3 173,648,841 173,650,940 NM_004122 − chr3-1158 GHSR 1 3 188,870,796 188,872,895 NM_001048 − chr3-3854 SST 1 4 5,102,428 5,104,527 NM_018401 + chr4-2546 STK32B 1 4 6,251,361 6,253,460 NR_037863 + chr4-6479 8 4 36,921,085 36,923,184 NM_001144990 + chr4-2028 KIAA1239 1 4 66,216,274 66,218,373 NR_034138 + chr4-3545 3 4 172,969,150 172,971,249 NM_001034845 + chr4-2412 GALNTL6 1 4 177,221,979 177,224,078 NM_181265 + chr4-8602 WDR17 1 4 177,221,979 177,224,078 NM_170710 + chr4-8602 WDR17 1 4 5,941,117 5,943,216 NM_001313 − chr4-18262 CRMP1 1 4 21,559,373 21,561,472 NM_147182 − chr4-7295 KCNIP4 1 4 36,919,909 36,922,008 NR_039965 − chr4-2028 NA NA 4 46,690,180 46,692,279 NM_001204267 − chr4-25870 GABRA4 1 4 46,690,180 46,692,279 NM_001204266 − chr4-25870 GABRA4 1 4 46,691,082 46,693,181 NM_000809 − chr4-25870 GABRA4 1 5 9,597,312 9,599,411 NR_045196 + chr5-27730 8 5 9,599,939 9,602,038 NR_003689 + chr5-18793 SNORD123 9 5 31,227,519 31,229,618 NM_004932 + chr5-19148 CDH6 1 5 140,765,954 140,768,053 NM_032100 + chr5-22310 PCDHGB6 1 5 140,765,954 140,768,053 NM_018926 + chr5-22310 PCDHGB6 1 5 145,696,780 145,698,879 NM_002700 + chr5-12906 POU4F3 1 5 1,498,444 1,500,543 NM_001044 − chr5-29013 SLC6A3 1 5 9,599,134 9,601,233 NM_003966 − chr5-18793 SEMA5A 1 5 36,027,193 36,029,292 NM_152404 − chr5-23479 UGT3A1 1 5 37,875,440 37,877,539 NM_000514 − chr5-12543 GDNF 1 5 63,293,203 63,295,302 NM_000524 − chr5-5052 HTR1A 1 5 158,459,267 158,461,366 NM_024007 − chr5-17366 EBF1 1 6 334,739 336,838 NM_001195286 + chr6-14328 IRF4 1 6 334,739 336,838 NM_001195286 + chr6-7903 IRF4 1 6 334,739 336,838 NM_002460 + chr6-14328 IRF4 1 6 334,739 336,838 NM_002460 + chr6-7903 IRF4 1 6 334,739 336,838 NR_036585 + chr6-14328 NA NA 6 334,739 336,838 NR_036585 + chr6-7903 NA NA 6 7,050,829 7,052,928 NM_001168344 + chr6-3985 RREB1 1 6 7,051,085 7,053,184 NM_001003699 + chr6-3985 RREB1 1 6 7,051,085 7,053,184 NM_001003698 + chr6-3985 RREB1 1 6 7,051,085 7,053,184 NM_001003700 + chr6-3985 RREB1 1 6 26,290,003 26,292,102 NM_003523 + chr6-10828 HIST1H2BE 1 6 27,912,419 27,914,518 NM_003520 + chr6-15406 HIST1H2BN 1 6 31,889,270 31,891,369 NM_005345 + chr6-16375 HSPA1A 1 6 43,718,745 43,720,844 NM_001193341 + chr6-9437 RSPH9 1 6 43,718,745 43,720,844 NM_152732 + chr6-9437 RSPH9 1 6 84,617,704 84,619,803 NM_001009994 + chr6-2200 RIPPLY2 1 6 117,691,414 117,693,513 NM_153453 + chr6-28662 VGLL2 1 6 117,691,414 117,693,513 NM_153453 + chr6-19743 VGLL2 1 6 117,691,430 117,693,529 NM_182645 + chr6-28662 VGLL2 1 6 117,691,430 117,693,529 NM_182645 + chr6-19743 VGLL2 1 6 133,602,188 133,604,287 NM_172105 + chr6-23296 EYA4 1 6 133,602,188 133,604,287 NM_172103 + chr6-23296 EYA4 1 6 133,602,188 133,604,287 NM_004100 + chr6-23296 EYA4 1 6 10,989,985 10,992,084 NM_004752 − chr6-25890 GCM2 1 6 27,913,997 27,916,096 NM_003510 − chr6-15406 HIST1H2AK 1 6 28,475,424 28,477,523 NR_028077 − chr6-6380 NA NA 6 28,475,424 28,477,523 NM_001163391 − chr6-6380 ZSCAN12 1 6 28,519,159 28,521,258 NM_001012455 − chr6-16740 ZSCAN23 1 6 28,662,992 28,665,091 NM_052923 − chr6-17652 SCAND3 1 6 31,890,715 31,892,814 NM_005527 − chr6-16375 HSPA1L 1 6 43,384,409 43,386,508 NM_206922 − chr6-7762 CRIP3 1 6 63,053,960 63,056,059 NM_152688 − chr6-1661 KHDRBS2 1 6 78,229,740 78,231,839 NM_000863 − chr6-26510 HTR1B 1 6 85,530,519 85,532,618 NM_001080508 − chr6-2768 TBX18 1 6 127,882,094 127,884,193 NM_001012279 − chr6-24682 C6orf174 1 6 137,857,125 137,859,224 NM_175747 − chr6-23012 OLIG3 1 6 170,441,523 170,443,622 NM_005618 − chr6-28480 DLL1 1 7 8,438,110 8,440,209 NM_152745 + chr7-4850 NXPH1 1 7 24,288,332 24,290,431 NM_000905 + chr7-28113 NPY 1 7 86,109,166 86,111,265 NM_000840 + chr7-19417 GRM3 1 7 86,810,887 86,812,986 NM_001243745 + chr7-11427 CROT 1 7 86,810,887 86,812,986 NM_001143935 + chr7-11427 CROT 1 7 86,810,887 86,812,986 NM_021151 + chr7-11427 CROT 1 7 96,471,226 96,473,325 NM_005222 + chr7-9141 DLX6 1 7 97,197,207 97,199,306 NM_003182 + chr7-15475 TAC1 1 7 97,197,207 97,199,306 NM_013998 + chr7-15475 TAC1 1 7 97,197,207 97,199,306 NM_013997 + chr7-15475 TAC1 1 7 97,197,207 97,199,306 NM_013996 + chr7-15475 TAC1 1 7 98,082,533 98,084,632 NM_002523 + chr7-38902 NPTX2 1 7 103,754,340 103,756,439 NM_199000 + chr7-28012 LHFPL3 1 7 120,754,326 120,756,425 NM_057168 + chr7-8399 WNT16 1 7 136,201,939 136,204,038 NM_001006630 + chr7-33041 CHRM2 1 7 136,201,939 136,204,038 NM_001006627 + chr7-33041 CHRM2 1 7 136,202,372 136,204,471 NM_000739 + chr7-33041 CHRM2 1 7 136,202,372 136,204,471 NM_001006632 + chr7-33041 CHRM2 1 7 136,202,372 136,204,471 NM_001006631 + chr7-33041 CHRM2 1 7 136,202,409 136,204,508 NM_001006626 + chr7-33041 CHRM2 1 7 136,202,524 136,204,623 NM_001006629 + chr7-33041 CHRM2 1 7 136,202,533 136,204,632 NM_001006628 + chr7-33041 CHRM2 1 7 150,735,255 150,737,354 NR_034013 + chr7-35552 5 7 150,735,255 150,737,354 NR_034012 + chr7-35552 NA NA 7 153,213,352 153,215,451 NM_001039350 + chr7-25866 DPP6 1 7 154,941,585 154,943,684 NM_001427 + chr7-16609 EN2 1 7 154,941,585 154,943,684 NM_001427 + chr7-12439 EN2 1 7 19,151,470 19,153,569 NM_152898 − chr7-40510 FERD3L 1 7 27,162,722 27,164,821 NM_006896 − chr7-23529 HOXA7 1 7 35,260,137 35,262,236 NM_001077653 − chr7-7931 TBX20 1 7 35,260,137 35,262,236 NM_001166220 − chr7-7931 TBX20 1 7 45,094,919 45,097,018 NM_001146334 − chr7-39912 NACAD 1 7 71,440,045 71,442,144 NM_001017440 − chr7-37359 CALN1 1 7 86,812,645 86,814,744 NR015381 − chr7-11427 TP53TG1 3 7 92,301,068 92,303,167 NM_001259 − chr7-36853 CDK6 1 7 93,041,879 93,043,978 NM_001742 − chr7-3549 CALCR 1 7 93,041,879 93,043,978 NM_001164737 − chr7-3549 CALCR 1 7 126,679,565 126,681,664 NM_001127323 − chr7-14832 GRM8 1 7 127,458,139 127,460,238 NM_022143 − chr7-30884 LRRC4 1 7 133,794,329 133,796,428 NM_001628 − chr7-5134 AKR1B1 1 7 150,737,958 150,740,057 NM_198285 − chr7-35552 WDR86 1 7 155,297,629 155,299,728 NM_000193 − chr7-10560 SHH 1 7 158,630,311 158,632,410 NM_003382 − chr7-27744 VIPR2 1 8 11,597,126 11,599,225 NM_002052 + chr8-16712 GATA4 1 8 13,466,723 13,468,822 NM_001007090 + chr8-27248 C8orf48 1 8 16,927,118 16,929,217 NM_181723 + chr8-4176 EFHA2 1 8 24,825,179 24,827,278 NM_005382 + chr8-4443 NEFM 1 8 24,826,360 24,828,459 NM_001105541 + chr8-4443 NEFM 1 8 50,985,150 50,987,249 NM_018967 + chr8-8079 SNTG1 1 8 55,531,048 55,533,147 NM_022454 + chr8-16602 SOX17 1 8 56,175,571 56,177,670 NM_052898 + chr8-10864 XKR4 1 8 56,175,571 56,177,670 NM_052898 + chr8-6273 XKR4 1 8 65,446,329 65,448,428 NR_034102 + chr8-10122 NA NA 8 65,446,329 65,448,428 NR_034102 + chr8-5087 NA NA 8 65,446,329 65,448,428 NR_034102 + chr8-15516 NA NA 8 65,450,438 65,452,537 NR_034103 + chr8-2210 3 8 65,452,260 65,454,359 NR_029669 + chr8-3384 MIR124-2 4 8 65,452,260 65,454,359 NR_029669 + chr8-2210 MIR124-2 4 8 67,505,272 67,507,371 NM_144650 + chr8-12141 ADHFE1 1 8 69,025,157 69,027,256 NM_025170 + chr8-7773 PREX2 1 8 69,025,157 69,027,256 NM_024870 + chr8-7773 PREX2 1 8 72,916,905 72,919,004 NR_033651 + chr8-26787 3 8 79,738,837 79,740,936 NM_016010 + chr8-29409 FAM164A 1 8 79,738,837 79,740,936 NM_016010 + chr8-14671 FAM164A 1 8 86,536,308 86,538,407 NM_005181 + chr8-4283 CA3 1 8 100,023,807 100,025,906 NM_001142462 + chr8-15618 OSR2 1 8 100,023,807 100,025,906 NM_053001 + chr8-15618 OSR2 1 8 121,204,533 121,206,632 NM_021110 + chr8-20019 COL14A1 1 8 23,619,957 23,622,056 NM_001136271 − chr8-13399 NKX2-6 1 8 24,869,949 24,872,048 NM_006158 − chr8-3960 NA NA 8 37,943,242 37,945,341 NM_000025 − chr8-13897 ADRB3 1 8 57,188,996 57,191,095 NM_005372 − chr8-3477 MOS 1 8 57,521,048 57,523,147 NM_006211 − chr8-5843 PENK 1 8 57,521,048 57,523,147 NM_006211 − chr8-15117 PENK 1 8 57,521,737 57,523,836 NM_001135690 − chr8-5843 PENK 1 8 57,521,737 57,523,836 NM_001135690 − chr8-15117 PENK 1 8 61,356,409 61,358,508 NM_004056 − chr8-10610 CA8 1 8 72,919,186 72,921,285 NM_005098 − chr8-26787 MSC 1 8 92,066,562 92,068,661 NM_001190972 − chr8-27696 1 8 92,066,562 92,068,661 NM_001190972 − chr8-20122 1 8 93,176,567 93,178,666 NM_001198630 − chr8-21157 RUNX1T1 1 8 93,176,567 93,178,666 NM_001198632 − chr8-21157 RUNX1T1 1 8 93,176,567 93,178,666 NM_175635 − chr8-21157 RUNX1T1 1 8 93,176,567 93,178,666 NM_001198629 − chr8-21157 RUNX1T1 1 8 93,176,959 93,179,058 NM_001198634 − chr8-21157 RUNX1T1 1 8 93,176,959 93,179,058 NM_001198626 − chr8-21157 RUNX1T1 1 8 93,176,959 93,179,058 NM_001198631 − chr8-21157 RUNX1T1 1 8 93,176,959 93,179,058 NM_001198627 − chr8-21157 RUNX1T1 1 8 93,176,959 93,179,058 NM_001198679 − chr8-21157 RUNX1T1 1 8 93,176,959 93,179,058 NM_001198625 − chr8-21157 RUNX1T1 1 8 93,180,993 93,183,092 NM_001198628 − chr8-17019 RUNX1T1 1 8 93,184,531 93,186,630 NM_001198633 − chr8-17019 RUNX1T1 1 8 93,184,531 93,186,630 NM_175634 − chr8-17019 RUNX1T1 1 8 97,242,097 97,244,196 NM_001001557 − chr8-10467 GDF6 1 8 105,548,354 105,550,453 NM_001385 − chr8-19479 DPYS 1 8 109,164,990 109,167,089 NM_178565 − chr8-27831 RSPO2 1 8 110,773,097 110,775,196 NM_001099744 − chr8-18053 SYBU 1 8 110,773,097 110,775,196 NM_001099743 − chr8-18053 SYBU 1 8 111,056,036 111,058,135 NM_014379 − chr8-4616 KCNV1 1 8 120,720,188 120,722,287 NM_001130863 − chr8-16413 ENPP2 1 8 120,720,188 120,722,287 NM_001040092 − chr8-16413 ENPP2 1 8 120,720,188 120,722,287 NM_006209 − chr8-16413 ENPP2 1 8 132,121,918 132,124,017 NM_001115 − chr8-3407 ADCY8 1 8 139,995,319 139,997,418 NM_152888 − chr8-8451 COL22A1 1 8 140,784,382 140,786,481 NM_016601 − chr8-19164 KCNK9 1 8 142,446,448 142,448,547 NM_005293 − chr8-21185 GPR20 1 8 144,312,733 144,314,832 NM_002347 − chr8-24646 LY6H 1 8 144,313,031 144,315,130 NM_001135655 − chr8-24646 LY6H 1 8 145,089,999 145,092,098 NM_201382 − chr8-29731 PLEC 1 8 145,090,794 145,092,893 NM_201381 − chr8-29731 PLEC 1 8 145,091,336 145,093,435 NR_030383 − chr8-29731 MIR661 4 8 145,530,652 145,532,751 NM_031309 − chr8-25100 SCRT1 1 9 17,123,038 17,125,137 NM_001114395 + chr9-8090 CNTLN 1 9 17,123,038 17,125,137 NM_017738 + chr9-8090 CNTLN 1 9 22,434,840 22,436,939 NM_022160 + chr9-24850 DMRTA1 1 9 32,771,497 32,773,596 NM_212558 + chr9-11022 TMEM215 1 9 70,976,791 70,978,890 NM_001170630 + chr9-33024 TJP2 1 9 70,976,791 70,978,890 NM_004817 + chr9-33024 TJP2 1 9 70,976,791 70,978,890 NM_201629 + chr9-33024 TJP2 1 9 73,952,087 73,954,186 NM_004293 + chr9-21502 GDA 1 9 73,952,087 73,954,186 NM_001242506 + chr9-21502 GDA 1 9 73,952,087 73,954,186 NM_001242505 + chr9-21502 GDA 1 9 78,822,391 78,824,490 NM_001013735 + chr9-28705 FOXB2 1 9 94,609,714 94,611,813 NR_026868 + chr9-9221 ANKRD19P 10 9 97,263,712 97,265,811 NR_038982 + chr9-12742 NA NA 9 16,860,687 16,862,786 NM_017637 − chr9-28476 BNC2 1 9 36,248,397 36,250,496 NM_005476 − chr9-31429 GNE 1 9 36,248,397 36,250,496 NM_001190384 − chr9-31429 GNE 1 9 36,248,397 36,250,496 NM_001190383 − chr9-31429 GNE 1 9 93,752,166 93,754,265 NM_004560 − chr9-25007 ROR2 1 9 95,148,418 95,150,517 NM_001098808 − chr9-33335 C9orf129 1 9 103,289,197 103,291,296 NM_032342 − chr9-7772 C9orf125 1 9 103,540,584 103,542,683 NM_133445 − chr9-21296 GRIN3A 1 9 132,803,961 132,806,060 NM_032843 − chr9-18934 FIBCD1 1 9 132,804,177 132,806,276 NM_001145106 − chr9-18934 FIBCD1 1 Transcript types are as follows: 1 = protein coding; 2 = unprocessing pseudogene; 3 = processed transcript; 4 = miRNA; 5 = antisense; 6 = pseudogene; 7 = ambiguous open reading frame; 8 = linRNA; 9 = snoRNA; 10 = transcribed unprocessed pseudogene.

Using the DAVID functional annotation tool to annotate this set of genes, two largely non-overlapping groups of genes were identified (FIGS. 9A and 9B).

One group is annotated with keywords “DNA-BINDING”, “TRANSCRIPTION”, “TRANSCRIPTION REGULATION”, “HOMEOBOX”, “DEVELOPMENTAL PROTEIN”, and “DIFFERENTIATIONS” and contains around 100 genes, mostly transcription factors, such as BARHL2, DLX6, OTX2, RUNX1T1 and TAC1. The second group is annotated with keywords “SIGNAL”, “CELL MEMBRANE”, “TRANSDUCER”, “GLYCOPROTEIN”, “G-PROTEIN COUPLED RECEPTOR” and contains genes involved in signaling pathways such as ADRB3, GHSR, NPY and ROBO3. These groups of genes are listed in Table 11.

To determine if promoter hypermethylation was potentially involved in gene silencing, TCGA expression data was assessed for the 308 genes affected by promoter hypermethylation as discussed previously. In doing so, it was shown that genes with promoter hypermethylation are enriched in down-regulated genes (71 out of 245 genes for which expression data is available are down-regulated; fold change (FC) of 1.73; p-value 1e-06) and are depleted in up-regulated genes (28 out of 245 genes are up-regulated; FC of 0.53; p-value 1e-05) (FIG. 1F).

The 308 hypermethylated genes were then overlapped with genes recurrently mutated in breast cancer in TCGA [TCGA (2012) Nature, 490:61-70] (FIG. 1G) to identify potential driver events. Of the 308 genes with promoter methylation, 51 were identified as being mutated (FC of 1.92; p-value 4.12e-06) and 12 (C9orf125, COL14A1, ENPP2, ERG2, PLD5, ROBO3, RUNX1T1, SEMA5A, TBX18, TSHZ3, ZBTB16, and ZNF208) were both mutated and down-regulated. Notably, of these gene subset which are both mutated and down-regulated, ROBO3 and SEMA5 are part of the axon guidance pathway recently implicated in tumor initiation and progression (Mehlen et al., (2011) Nat. Rev. Cancer 2011, 11:188-197; Neufeld and Kessler (2008) Nat. Rev. Cancer, 8:632-645). Interestingly, promoter hypermethylation affects, in total, seven members of the axon guidance pathway (CRMP1, GDNF, GFRA1, MYL9, ROBO1, ROBO3 and SEMA5A) with four members (GFRA1, MYL9, ROBO3, and SEMA5A) being down-regulated.

TABLE 11 DAVID functional analysis of genes with promoter hypermethylation List Pop Pop Fold Category Term Count % PValue Genes Total Hits Total Enrichment Bonferroni Benjamini FDR SP_PIR_KEY dna-binding 86 29 9.35E−18 BARHL2, BATF3, BHLHE23, DLX6, DMRTA1, DMRTA2, EBF1, EN2, EOMES, 284 1628 14208 2.64 2.65E−15 2.65E−15 1.24E−14 WORDS ERG, EVX2, FERD3L, FE2F2, FOXB1, FOXB2, FOXF1, GATA2, GATA4, GOM2, GSC2, GSX1, HIST1H2AX, HIST1H2BE, HIST1H2BN, HMX2, HOXA7, HOXB13, HOXD10, HOXD13, IRF4, LHX5, M5C, MYOD1, NEUROD1, NEUROG3, NKX2-1, NKZ2-3, NKX2-6, NKX6-2, OLIG2, OLIG3, OTX3, PAX1, POU4F3, PPARG, PROM16, PRRX1, PTF1A, RREB1, RUNXIT1, SALL2, SALL3, SATB2, SCRT1, SIM2, SLC2A4RG, SOX17, SOX21, TBX18, TBX20, TSHZ3, VAX1, VENTX, VSX1, WT1, ZBTB16, ZIC1, ZIK1, ZNF154, ZNF215, ZNF221, ZNF229, ZNF254, ZNF334, ZNF418, ZNF502, ZNF506, ZNF516, ZNF540, ZNF542, ZNF560, ZNF671, ZNF773, ZNF98, ZSCAN18, ZSCAN23 SP_PIR_KEY developmental 46 15 5.88E−13 C140RF39, CHL1, CHRDL2, CSPG4, DAB1, DDX25, DLL1, DLX6, EBF1, 284 671 14208 3.43 1.66E−10 8.32E−11 7.79E−10 WORDS protein EN2, EOMES, EVX2, EYA4, FEZF2, GCM2, GSX1, HMX2, HOXA7, HOXB13, HOXD10, HOXD13, ITGA5, MYOD1, NEUROD1, NEUROG3, NKX6-2, OLIG2, OTX2, PAX1, PRRK1, PTF1A, RIPPLY2, ROBO1, ROBO3, ROR2, SATB2, SEMA5A, SHH, SIM2, TSHZ3, VAX1, VENTX, VSX1, WNT16, WNT7A, ZIC1 SP_PIR_KEY Homeobox 24 8 1.16E−11 BARHL2, DLX6, EN2, EVX2, GSC2, GSX1, HMXZ, HOXA7, HOXB13, 284 200 14208 6.00 3.29E−09 1.10E−09 1.54E−08 WORDS HOXO10, HOXD13, UHX5, NKX2-1, NKX2-3, NKX2-6, NKX6-2, OTX2, POU4F3, PRRX1, SATB2, TSHZ3, VAX1, VENTZ, VSK1 SP_PIR_KEY transcription 77 26 4.13E−11 BARHL2, BATF3, BHLHE23, BNC2, DMRTA1, EBF1, EID1, EN2, EOMES, 284 1780 14208 2.16 1.17E−08 2.93E−09 5.48E−08 WORDS regulation ERG, EYA4, FERDJL, FEZF2, FOXB1, FOXB2, FOXF1, GATA2, GATA4, GCM2, GSX1, HMX2, HOXA7, HOXB13, HOXD10, HOXD13, IRF4, KHDRBS2, LHXS, MSC, MYOD1, NEUROD1, NEUROG3, NKX2-1, NKX2-3, OLIG2, OLIG3, PAX1, PPARG, PRDM16, PTF1A, RREB1, RUNX1T1, SALL2, SALL3, SATB2, SCRT1, SIM2, SLC2A4RG, SOX17, SOX21, TBX18, TBX20, TSHZ3, VAX1, VGLL2, VSX1, WT1, ZBTB16, ZIK1, ZNF154, ZNF215, ZNF221, ZNF229, ZNF254, ZNF334, ZNF418, ZNF502, ZNF506, ZNF516, ZNF540, ZNF542, ZNF560, ZNF671, ZNF773, ZNF98, ZSCAN18, ZSCAN23 SP_PIR_KEY Transcription 76 25 3.18E−10 BARHL2, BATF3, BHLHE23, BNC2, DMRTA1, EBF1, EID1, EOMES, ERG, 284 1821 14208 2.09 9.01E−08 1.80E−08 4.22E−07 WORDS EYA4, FERD3L, FEZF2, FOXB2, FOXB2, FOXF1, GATA2, GATA4, GCM2, GSX1, HMX2, HOXA7, HOXB13, HOXD10, HOXD13, IRF4, KHDRBS2, LHXS, MSC, MYOD1, NEUROG3, NEUROG3, NKX2-1, NKX2-3, OLIG2, OLIG3, PAX1, PPARG, PRDM16, PTF1A, RREB1, RUNX1T1, SALL1, SALL3, SATB2, SCRT1, SIM2, SLC2A4RG, SOX17, SOX21, TBX18, TBX20, TSHZ3, VAK1, VGLL2, VSX1, WT1, ZBTB16, ZIK1, ZNF154, ZNF215, ZNF221, ZNF229, ZNF254, ZNF334, ZNF418, ZNF502, ZNF506, ZNF516, ZNF540, ZNF542, ZNF560, ZNF671, ZNF773, ZNF98, ZSCAN18, ZSCAN23 SP_PIR_KEY glycoprotein 99 33 6.00E−10 ACP5, ADCY3, ADRB3, AGTR1, AMIGO2, APCDD1, BONF, C11ORF87, 284 2739 14208 1.81 1.70E−07 2.83E−08 7.95E−07 WORDS CA4, CACNA1E, CALCR, CASR, CBLN1, CBLN4, CD200, CD70, CDH13, CDH6, CDH8, CHL1, CHRDL2, CHRM2, CNTNAP5, COL11A1, COL14A1, COL22A1, CSPG4, DLL1, OPP6, EMILIN2, ENPP2, EPHA10, FAM155A, FBN1, GABRA4, GALNTL6, GALR1, GDF6, GDNF, GFRA1, GHSR, GPR139, GPR158, GPR20, GRIK3, GRIN3A, GRM3, GRM8, HAPLN3, HS3ST2, HTR1A, HTR1B, IGSF9B, ITGA8, JAM3, KCNC2, KCNK9, LRRC4, LV6H, MLNR, NEFM, NELL1, NETO1, NLGN1, NPR1, NFTX2, NTM, NXPH1, OPCML, OPRL1, PCDH17, PCCH8, PLD5, PROKR2, PTPRN, ROBO1, ROBO3, ROR2, RSPO2, RYR2, S1PR5, SEMA5A, SHH, SLC18A3, SLC24A4, SLC6A3, SLC7A14, SORCS3, ST8S1AS, SYNPR, TMEFF2, TMEM132C, TMEM132D, TRHDE, UGT3A1, VIPR2, WNT16, WNT7A SP_PIR_KEY signal 75 25 3.57E−07 ACP5, AMIGO2, APCDD1, BDNF, C11ORF87, C6ORF174, CA4, CALCR, 284 2101 14208 1.79 1.01E−04 1.44E−05 4.73E−04 WORDS CASR, CBLN1, CBLN4, CD200, CDH13, CDH6, CDH8, CHL1, CHRDK2, CNTNAP5, COL11A1, COL14A1, CSPG4, DLL1, EMILIN2, ENPP2, EPHA10, FAM150B, FBN1, GABRA4, GDF6, GDNF, GFRA1, GPR158, GRIK3, GRIN3A, GRM3, GRM8, HAPLN3, IG5F9B, ITGA8, JAM3, LRRC4, LY6H, NELL1, NETO1, NLGN1, NPR1, NPTX2, NPY, NTM, NXFH1, OPCML, PCDH17, PCDH8, PENK, PTPRN, RIC3, ROBO1, ROBO3, ROR2, R5POZ, SEMA54, SHH, SLC24A4, SDRCS3, SST, TAC1, TMEFF2, TMEM132C, TMEM132D, UGT3A1, VIPR2, VSTM2B, WNT16, WNT7A SP_PIR_KEY cell membrane 54 18 9.43E−07 ADRB3, AGTR1, AMIGO2, ANK51B, APC2, CA4, CALCR, CALN1, CA5R, 284 1344 14208 2.01 2.67E−04 3.33E−05 1.25E−03 WORDS CD200, CDH13, CDH6, CDH8, CHL1, CHRM2, CSPG4, DGKZ, EPHA10, GABRA4, GAD2, GALR1, GFRA1, GH5R, GID2, GPR139, GPR158, GPR20, GRIK3, GRIN3A, GRM3, GRM8, HTR1A, HTR1B, IGSF9B, JAM3, JPH3, KCNIP4, KCNV1, LRRC4, LY8H, MLNR, NETO1, NLGN1, NTM, OPCML, OPRL1, PCDH17, PCDH8, PROKR2, S1PR5, SHH, T3P2, UNC13A, VIPR2 SP_PIR_KEY disulfide bond 61 20 1.49E−06 ACP5, ADRB3, AGTR1, AMIGO2, BDNF, CA4, CACNA1E, CBLN1, CBLN4, 284 1628 14208 1.87 4.22E−04 4.69E−05 1.97E−03 CD200, CD70, CHL1, CHRM2, CNTNAP5, CDL14A1, CSPG4, DLL1, DPP6, EMIL1N2, ENPP2, FBN1, GABRA4, GALNTL5, GALR1, GDF6, GDNF, GFRA1, GHSR, HAPLN1, H53ST2, HTR1A, HTR1B, IG5F9B, ITGA8, JAM3, LRRC4, LY6H, MLNR, NELL1, NETO1, NLGN1, NPR1, NPTX2, NTM, OPCML, OPRL1, PENK, PROKR2, ROBO1, ROBO3, ROR2, RSPO2, SEMA54, SLC6A3, SST, WORDS ST851A3, ST851A3, TMEFF2, TRHDE, VIPR2, VSTM2B SP_PIR_KEY g-protein 19 6 7.21E−06 ADRB3, AGTR1, CALCR, CASR, CHRM2, GALR1, GHSR, GPR139, GPR158, 284 268 14208 3.55 2.04E−03 2.04E−04 9.55E−03 WORDS coupled receptor GPR20, GRM3, GRM8, HTR1A, HTR1B, MLNR, OPRL1, PROKR2, S1PR5, VIPR2 SP_PIR_KEY transducer 20 7 1.79E−05 ADRB3, AGTR1, CALCR, CASR, CHRM2, CSPG4, GALR1, GHSR, GPR139, 284 314 14208 3.19 5.05E−03 4.60E−04 2.37E−02 WORDS GPR158, GPR20, GRM3, GRM8, HTR1A, HTR1B, MLNR, OPRL1, PROKR2, S1PR5, VIPR2 SP_PIR_KEY differentiation 22 7 2.71E−05 CHL1, CHRDL2, CSPG4, DAB1, DOX25, DLL1, DMRTA1, EID1, EDMES, 284 362 14208 2.88 7.54E−03 6.39E−04 3.59E−02 WORDS FEZF2, HMX2, ITGAB, MYDDI, NEURDD1, NEURDG3, PRDM16, PTF14, ROBO1, ROBO3, SEMA5A, SIM2, ZIC3

1.2.5 Differentially Methylated Regions Specific to TNBCs

To determine if any of the 822 DMRs were also found in ER−ve or ER+ve breast cancer, the TCGA breast cancer methylation cohort, which comprises HM450K data for 354 ER+ve and 105 ER−ve breast tumors (73 of which are TNBCs) and 83 normal breast samples, was interrogated. Of the 822 hypermethylated DMRs identified using MBDCap-Seq, it was determined that 770 are interrogated by a total of 4,987 HM450K probes. It was also determined that whilst the majority of these probes are not methylated in breast normal tissue, they were hypermethylated to various degrees in both ER+ve and ER−ve breast cancers (FIG. 2A). Both ER+ve and ER−ve subtypes were also found to contain samples with minimal methylation across all probes, as well as those that displayed extensive methylation more representative of a CpG island methylator phenotype (CIMP) (Hughes et al., (2013) Cancer Research).

Out of 4,987 HM450K probes, it was determined that 5% (282/4,987) were significantly hypermethylated in TNBCs (t-test adjusted p-value less than 0.05) compared to the ER+ve tumors and the rest of the ER−ve tumors. From the 282 TNBC-specific probes, 36 TNBC-specific regions (harbouring at least 3 or more 450K TNBC-specific probes) were identified, that primarily overlap promoters and/or gene bodies (Table 2 and FIG. 10). These regions are enriched in genes encoding zinc fingers and transcription factors and intergenic regions that are commonly marked by polycomb in HMECs. An example of two such TNBC specific regions, are located in the promoters of genes encoding zinc finger proteins ZNF154 and ZNF671 on chromosome 9 (FIG. 2B). Both promoters have low methylation levels in normal breast and increased levels of methylation in TNBC samples as compared to ER+ve cancer. It was also found that the distribution of expression values mirrors the methylation status, with normal samples showing the highest levels of expression and TNBC tumors showing the lowest levels of expression (FIG. 2C), suggesting silencing by methylation of both ZNF154 and ZNF671 in TNBC tumors.

Example 2: Prognostic Markers of Patient Outcome

2.1 Methods

To identify DMRs that potentially stratify TNBCs, unsupervised cluster analysis was performed on methylation data for the 4,987 HM450K probes. A survival analysis was then performed to determine to what extent regional methylation stratifies TNBCs into good and bad prognosis groups.

2.1.1. Unsupervised Clustering

The TCGA TNBC (n=73) tumor samples were clustered based on methylation beta-values of 4,987 HM450K probes overlapping the 822 hypermethylated regions. A consensus clustering algorithm (Monti et. al., Machine Learning 2003) i.e., as implemented in Bioconductor Consensus Cluster Plus package to the 4,987×73 methylation matrix with parameters max K=4, reps=1000, pltem=0.8, pFeature=0.8, clusterAlg=“km”, distance=“euclidean”, was then applied. SVD decomposition was used to reduce the dimension of the methylation matrix to R¹⁰ prior to clustering. A three-cluster configuration was chosen for downstream survival analysis.

2.1.2 Survival Analysis

Survival analysis was carried out using Cox proportional hazards model as implemented in R survival package against overall survival data. The BRCA TNBC cohort consists of 73 patients with HM450K methylation data and 12 events. Survival analysis of cluster data was carried out with cluster membership as an explanatory variable. Survival analysis of individual probes was carried out with probe methylation status as explanatory variable (univariate analysis) and age, stage and probe methylation status (multivariate analysis). Methylation status was represented by a binary variable, high (higher that the median beta-value for the probe) and low (smaller or equal to the median beta-value for the probe). Stage was derived from AJCC stage in the clinical annotation of samples. Due to moderate size the cohort we reduced the number of values of the stage variable to two by collapsing stages I, IA, IB, II, IIA, and IIB into one state and stages III, IIIA, IIIB, IIIC, and IV into one state.

2.2 Results

2.2.1 Stratification of TNBCs

The unsupervised clustering analysis identified three distinct groups of TNBC tumors from the TCGA data sets (FIG. 3A).

Survival analysis revealed that the largely hypomethylated cluster (blue cluster) was associated with better prognosis as compared to the other two more highly methylated clusters (orange and red clusters) (FIG. 3B). In particular, the medium methylated cluster (orange cluster) comprises samples with the worst prognosis (hazard ratio of 8.64 and p-value of 0.005) as compared to the good prognosis cluster (blue cluster).

2.2.2 TNBC Methylation Prognostic Signature(s)

Survival analysis of the 4,987 HM450K probes overlapping 822 hyper-methylated DMRs identified 190 probes with methylation status statistically significantly (p-value<0.05 in both univariate and multivariate analyses Cox Proportional Hazard models) associated with overall survival in TCGA TNBC samples. Furthermore, regional aggregation of survival probes identified 17 hyper-methylated DMRs overlapping three or more survival probes i.e., at least three concordantly located survival probes. In particular, fourteen DMRs were associated with poor prognosis, these regions overlapped probes for which high methylation corresponded to lower probability of survival, and three regions were associated with good prognosis (Table 3 and FIG. 11). Each of the individual Kaplan Meier plots showed excellent survival separation, highlighting the potential value of probes overlapping the 17 hyper-methylated DMRs as prognostic biomarkers for TNBC (FIGS. 3C-E and FIG. 11). Critically, our classifiers paralleled the biologically relevant time-dependent pattern of patient outcome, whereby TNBC patients are most vulnerable to disease associated death within the first 5 years following diagnosis, further highlighting their potential use in prognostic applications for TNBC.

The genomic location of the 17 hyper-methylated DMRs vary, with four regions located in a promoter (SLC6A3, C6orf174, WT1-AS and ZNF254), seven in the gene body only (DMRTA2, LHX8, WT1, WT1-AS, HOXB13, ECEL1, SOX2-OT) and five in intergenic regions (Table 3).

A striking example of regional hyper-methylation across consecutive CpG probes that provides statistical significance as a prognostic marker of survival are the DMRs spanning the bidirectional promoter and gene bodies of WT1 gene and its antisense counter-part, WT1-AS (FIG. 3F). Wilms tumour protein (WT1) is a zinc finger transcription factor over-expressed in several tumour types including breast (reviewed in Yang et al., (2007) Leukemia, 21:868-876). In the present study, an association was observed between a high level of methylation in chr11-11623 and chr11-1210 i.e., regions spanning the gene bodies of WT1 and WT1-AS respectively, and poor survival in TCGA TNBC cohort (FIG. 3F). Moreover, increased levels of methylation in these regions was also found to be associated with increased expression of WT1 (chr11-11623) and WT1-AS (chr11-1210) in TNBC patients (FIG. 12). Conversely, TNBC patients with high methylation in chr11-4047 i.e., a region spanning bi-directional promoter of WT1 and WT1-AS, were found to survive longer than TNBC patients with low methylation in this region.

Those skilled in the art will appreciate that the disclosure described herein is susceptible to variations and modifications other than those specifically described. It is to be understood that the disclosure includes all such variations and modifications. The disclosure also includes all of the steps, features, compositions and compounds referred to or indicated in this specification, individually or collectively, and any and all combinations or any two or more of said steps or features. It will be appreciated by persons skilled in the art that numerous variations and/or modifications may be made to the above-described embodiments, without departing from the broad general scope of the present disclosure. The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive. Functionally-equivalent products, compositions and methods are clearly within the scope of the disclosure, as described herein.

Example 3: TNBC-Specific Methylation

3.1 Methods

ROC curve analyses were performed using methylation data obtained for the 282 TNBC-specific probes (identified in Example 1) in the TCGA HM450K cohort to determine the accuracy with which those probes can be used to classify tumour samples into TNBC and non-TNBC.

The TCGA HM450K cohort was randomly split into training set (TNBC n=37; non-TNBC n=193) and testing set (TNBC n=36; non-TNBC n=193). The model was trained on training set and prediction accuracy assessed on testing set.

3.2 Results

ROC curve analysis showed that the diagnostic methylation signature for the 282 TNBC-specific probes can classify TCGA HM450K tumour samples into TNBC and non-TNBC with high accuracy i.e., sensitivity of 0.72, specificity of 0.94 and AUC of 0.90 (FIG. 13).

Example 4: Validation of the TNBC Diagnostic Methylation Signature on an Independent Cohort of Clinical Samples

4.1 Methods

Methylation signatures of an independent cohort of TNBC clinical samples obtained from The Garvan Institute, The University of Queensland (UQ) and The University of Newcastle (The NBCF cohort) were determined using HM450K arrays and methodologies described in Example 1. The NBCF cohort comprised 47 patient samples which passed quality control (QC); 24 events and 23 non-events.

4.2 Results

Based on the HM450K methylation data obtained for the NBCF cohort, the diagnostic methylation signature of the 282 TNBC-specific probes trained on TCGA data was able to classify 93% (44/47) samples from the NBCF cohort as TNBC (FIGS. 14 and 15).

Example 5: Enumerating Diagnostic Signatures

5.1 Methods

Based on the 282 TNBC-specific probes identified in Example 1 as having higher average methylation in TNBC tumours relative to non-TNBC tumours, the inventors then sought to identify small subsets of probes that could be used in combination with Machine Learning Techniques to achieve accurate separation between TNBC and non-TNBC tumours.

5.1.1 Modelling Approach

A Partial Least Squares (PLS) family of models was selected for classification and caret R package (https://cran.r-project.org/web/packages/caret/index.html; Max Kuhn and Kjell Johnson, Adaptive Predictive Modeling, Springer 2013) used to train and evaluate the PLS models.

Model selection and parameter estimation was performed on the train dataset and final model evaluation was performed on the test dataset. The train dataset included all TCGA TNBC tumour samples (n=73) and half of TCGA non-TNBC tumour samples (n=193; 117 ER+ve and 16 non-TNBC ER−ve samples). The test dataset included all NBCF TNBC tumour samples (n=47) and the other half of TCGA non-TNBC tumour samples (n=193; 117 ER+ve and 16 non-TNBC ER−ve samples). In some plots information from TCGA normal samples (n=83) and whole blood samples (n=11; GSE48472) was also included.

5.1.2 Signature Enumeration Strategy

A simple greedy strategy was devised to enumerate a large number of probe combinations (two to four probes per panel) resulting in PLS models with good performance on the train dataset. We first trained a PLS model on full subset of 256 probes (26 of the 282 probes were removed from the analyses due to the presence of missing values in some of the samples) and ranked the probes using PLS in-built variable importance estimates (function caret::varImp) (FIG. 16).

Two-probe signatures were formed by training PLS models on all three possible combinations of two probes out of three most important probes. The two-probe signatures were then extended to three-probe signatures by adding one probe at a time and keeping solutions that resulted in statistically significant improvement of AUC on train dataset (function pROC::roc.test). To reduce the amount of computation, only the twenty most important probes were considered during the extension step. Using this approach, four-probe signatures were also created by extending three-probe signatures in a similar manner. This procedure resulted in 56 possible signatures as shown in FIG. 17.

5.2 Results

Based on the data presented in FIG. 17, it is apparent that some three-probe and four-probe signatures performed as well as full PLS model on test dataset.

A summary of the 56 possible signatures is provided in Table 12.

TABLE 12 Summary of different probe subsets evaluated No. of Subset ID Probes in subsets AUC Probes FIG. 1 cg08048222 + cg09368188 0.938 2 18 2 cg00421363 + cg09368188 0.929 2 19 3 cg00421363 + cg08048222 0.908 2 20 4 cg04781584 + cg08048222 + cg09368188 0.959 3 21 5 cg03559454 + cg08048222 + cg09368188 0.955 3 22 6 cg08048222 + cg09368188 + cg23524195 0.952 3 23 7 cg08048222 + cg09368188 + cg10884788 0.952 3 24 8 cg08048222 + cg09368188 + cg13473196 0.951 3 25 9 cg00421363 + cg08048222 + cg09368188 0.949 3 26 10 cg00421363 + cg09368188 + cg09777776 0.949 3 27 11 cg01126567 + cg08048222 + cg09368188 0.948 3 28 12 cg00421363 + cg09368188 + cg23524195 0.948 3 29 13 cg00421363 + cg09368188 + cg10884788 0.947 3 30 14 cg00421363 + cg02286642 + cg09368188 0.945 3 31 15 cg07385362 + cg08048222 + cg09368188 0.945 3 32 16 cg08048222 + cg09368188 + cg22562461 0.944 3 33 17 cg01926238 + cg08048222 + cg09368188 0.943 3 34 18 cg05650260 + cg08048222 + cg09368188 0.943 3 35 19 cg00421363 + cg04416734 + cg09368188 0.942 3 36 20 cg00421363 + cg05650260 + cg09368188 0.942 3 37 21 cg00421363 + cg01126567 + cg09368188 0.940 3 38 22 cg04416734 + cg08048222 + cg09368188 0.940 3 39 23 cg00421363 + cg07385362 + cg09368188 0.939 3 40 24 cg00421363 + cg01926238 + cg09368188 0.936 3 41 25 cg00421363 + cg08048222 + cg23524195 0.917 3 42 26 cg00421363 + cg04416734 + cg08048222 0.906 3 43 27 cg01926238 + cg03559454 + cg08048222 + cg09368188 0.954 4 44 28 cg03559454 + cg05650260 + cg08048222 + cg09368188 0.954 4 45 29 cg04416734 + cg04781584 + cg08048222 + cg09368188 0.953 4 46 30 cg04416734 + cg08048222 + cg09368188 + cg23524195 0.953 4 47 31 cg00421363 + cg05650260 + cg09368188 + cg23524195 0.951 4 48 32 cg00421363 + cg09368188 + cg09777776 + cg23524195 0.951 4 49 33 cg00421363 + cg05650260 + cg09368188 + cg10884788 0.950 4 50 34 cg05650260 + cg08048222 + cg09368188 + cg13473196 0.950 4 51 35 cg00421363 + cg02286642 + cg09368188 + cg23524195 0.949 4 52 36 cg01926238 + cg08048222 + cg09368188 + cg13473196 0.949 4 53 37 cg00421363 + cg04416734 + cg08048222 + cg09368188 0.949 4 54 38 cg00421363 + cg07385362 + cg08048222 + cg09368188 0.949 4 55 39 cg00421363 + cg09368188 + cg09777776 + cg10884788 0.949 4 56 40 cg00421363 + cg05650260 + cg08048222 + cg09368188 0.948 4 57 41 cg00421363 + cg08048222 + cg09368188 + cg22562461 0.948 4 58 42 cg08048222 + cg09368188 + cg13473196 + cg22562461 0.948 4 59 43 cg04416734 + cg08048222 + cg09368188 + cg13473196 0.948 4 60 44 cg01126567 + cg05650260 + cg08048222 + cg09368188 0.947 4 61 45 cg00421363 + cg02286642 + cg09368188 + cg10884788 0.947 4 62 46 cg00421363 + cg01926238 + cg09368188 + cg23524195 0.945 4 63 47 cg00421363 + cg01126567 + cg05650260 + cg09368188 0.944 4 64 48 cg00421363 + cg01126567 + cg09368188 + cg09777776 0.943 4 65 49 cg00421363 + cg05650260 + cg09368188 + cg13473196 0.941 4 66 50 cg00421363 + cg01126567 + cg02286642 + cg09368188 0.940 4 67 51 cg00421363 + cg04416734 + cg05650260 + cg09368188 0.939 4 68 52 cg04416734 + cg07385362 + cg08048222 + cg09368188 0.939 4 69 53 cg00421363 + cg01926238 + cg09368188 + cg13473196 0.932 4 70 54 cg00421363 + cg01926238 + cg08048222 + cg23524195 0.927 4 71 55 cg00421363 + cg04416734 + cg08048222 + cg23524195 0.912 4 72 56 cg00421363 + cg04416734 + cg08048222 + cg20095233 0.905 4 73 57 s5 0.950 4 74 58 s6 0.953 6 75 59 s10 0.954 10 76 60 s20 0.950 20 77 61 full 0.952 256 78

Detailed performance summaries of (i) three representative two-probe signatures which performed particularly well are provided in FIGS. 18-20, respectively, (ii) three representative three-probe signatures which performed particularly well are provided in FIGS. 21-23, respectively, and (iii) three representative four-probe signatures which performed particularly well are provided in FIGS. 44-46, respectively. Each of FIGS. 18-23 and 44-46 show the distribution of methylation values across the probes in the model, as well as ROC curves and associated AUC statistics for diagnosing TNBC using the respective probe subset. Similar performance summaries for the remaining unique diagnostic signatures described in Table 12 are provided in FIGS. 24-43 and 45-78.

Models which performed particularly well comprised the following HM450K probes:

-   -   cg0804822—promoter of ZNF671     -   cg09368188—intronic region of KIF26B     -   cg00421363—promoter region of PPFIA3     -   cg04781584—promoter region of PPFIA3     -   cg03559454—promoter region of PPFIA3     -   cg01926238—distant promoter of VGLL2     -   cg05650260—intronic region of TFAP2D     -   cg04416734—exonic region of ALDOA

Example 6: Enumerating Diagnostic Signatures With Discrete Methylation Values

6.1 Method

In this example, the inventors determined whether the numerical methylation values (fraction of methylated molecules which is between 0 and 1) could be reduced from the HM450K array to three methylation categories (Category_1: 0.00-0.25, Category_2: 0.25-0.50, and Category_3: 0.50-1.00) to increase the applicability in the clinical setting.

6.1.1 Modelling and Signature Enumeration Strategies

Computational approaches similar to those described in Example 5 were used to model and enumerate the diagnostic signatures. The only modification to the approach previously described is that probes that showed methylation values greater than 25% in 10 or more tissue normal samples were also excluded. This further exclusion resulted in subset of 181 probes.

6.2 Results

40 unique diagnostic signatures were identified using the approach described (FIG. 79). Based on the data presented in FIG. 79, it is apparent that a number of the three-probe and four-probe diagnostic signatures performed almost as well as the full PLS model on the test dataset.

A summary of the 40 possible diagnostic CpG methylation signatures is provided in Table 13.

TABLE 13 Summary of different diagnostic CpG methylation signatures Subset No of ID Probes in subset AUC Probes FIG. 1 cg11977686 + cg13484549 0.897 2 80 2 cg08048222 + cg13484549 0.895 2 81 3 cg00421363 + cg08048222 0.868 2 82 4 cg00421363 + cg11977686 0.867 2 83 5 cg00421363 + cg13484549 0.825 2 84 6 cg08048222 + cg11977686 0.798 2 85 7 cg01926238 + cg08048222 + cg13484549 0.927 3 86 8 cg08048222 + cg08398233 + cg13484549 0.911 3 87 9 cg00421363 + cg01926238 + cg08048222 0.908 3 88 10 cg08048222 + cg13484549 + cg20095233 0.902 3 89 11 cg08048222 + cg13484549 + cg23524195 0.901 3 90 12 cg00421363 + cg08048222 + cg17268801 0.898 3 91 13 cg08048222 + cg13473196 + cg13484549 0.898 3 92 14 cg00421363 + cg08048222 + cg20095233 0.894 3 93 15 cg00421363 + cg08048222 + cg23524195 0.892 3 94 16 cg00421363 + cg08048222 + cg08398233 0.889 3 95 17 cg00421363 + cg08048222 + cg13473196 0.873 3 96 18 cg01926238 + cg07802350 + cg08048222 + cg13484549 0.926 4 97 19 cg01926238 + cg08048222 + cg13484549 + cg20095233 0.924 4 98 20 cg01926238 + cg08048222 + cg13473196 + cg13484549 0.924 4 99 21 cg01926238 + cg07091412 + cg08048222 + cg13484549 0.924 4 100 22 cg01926238 + cg08048222 + cg13484549 + cg23524195 0.921 4 101 23 cg01926238 + cg02809746 + cg08048222 + cg13484549 0.921 4 102 24 cg02286642 + cg08048222 + cg13484549 + cg20095233 0.919 4 103 25 cg08048222 + cg08398233 + cg13484549 + cg19717586 0.917 4 104 26 cg00421363 + cg02286642 + cg08048222 + cg20095233 0.914 4 105 27 cg00421363 + cg01926238 + cg08048222 + cg20095233 0.914 4 106 28 cg08048222 + cg08398233 + cg13473196 + cg13484549 0.913 4 107 29 cg00421363 + cg01926238 + cg08048222 + cg23524195 0.912 4 108 30 cg00421363 + cg08048222 + cg17268801 + cg20095233 0.912 4 109 31 cg01926238 + cg08048222 + cg08398233 + cg13484549 0.911 4 110 32 cg00421363 + cg01926238 + cg07091412 + cg08048222 0.910 4 111 33 cg00421363 + cg08048222 + cg17268801 + cg23524195 0.909 4 112 34 cg08048222 + cg08398233 + cg13484549 + cg23524195 0.907 4 113 35 cg00421363 + cg01926238 + cg08048222 + cg13473196 0.905 4 114 36 cg00421363 + cg01926238 + cg02809746 + cg08048222 0.904 4 115 37 cg08048222 + cg08398233 + cg13484549 + cg20095233 0.903 4 116 38 cg00421363 + cg01926238 + cg08048222 + cg08398233 0.899 4 117 39 cg00421363 + cg08048222 + cg08398233 + cg20095233 0.897 4 118 40 cg00421363 + cg08048222 + cg13473196 + cg20095233 0.896 4 119 41 s5 0.918 5 120 42 s6 0.918 6 121 43 s10 0.916 10 122 44 s20 0.917 20 123 45 full 0.934 181 124

Detailed performance summaries of the (i) three representative two-probe diagnostic signatures which performed particularly well are provided in FIGS. 80-82, respectively, (ii) three representative three-probe diagnostic signatures which performed particularly well are provided in FIGS. 86-88, respectively, and (iii) three representative four-probe diagnostic signatures which performed particularly well are provided in FIGS. 97-99, respectively. Each of FIGS. 80-82, 86-88 and 97-99 provides the distribution of methylation values across probes in the respective model, as well as ROC curves and associated AUC statistics for diagnosing TNBC using the respective probe subset. Similar performance summaries for the remaining unique diagnostic signatures described in Table 13 are provided in FIGS. 83-85, 89-96 and 100-118.

Models which performed particularly well comprised the following HM450K probes:

-   -   cg11977686—promoter of ZNF671     -   cg13484549—promoter of PPFIA3     -   cg08048222—promoter of ZNF671     -   cg00421363—promoter of PPFIA3     -   cg01926238—distant promoter of VGLL2     -   cg08398233—intronic region of CAMK2N1     -   cg07802350—distant promoter of HOXD13     -   cg20095233—distant promoter of GFRA1     -   cg13473196—intronic region of CKAP5

Example 7: Validation of the TNBC Prognostic Methylation Signature on an Independent Cohort of Clinical Samples

7.1 Method

7.1.1 Methylation Signature for NBCF Cohort

Methylation signatures were determined for the NBCF cohort using HM450K arrays and methodologies described in Example 1. The NBCF cohort used to validate TNBC prognostic methylation signatures comprised a total of 62 patients which passed HM450 array QC and had a complete set of clinical data. Events had a median time to death of 28.5 months (range 7-82 months) and non-events had a median follow up time of 61.5 months (range 7-194 months) (FIG. 125).

7.1.2 Survival Analysis

Survival analysis was performed in accordance with methods described previously at Example section 2.1.2 for all MBDCap regions that contained at least one HM450 probe that was prognostically significant in the discovery cohort.

7.1.3 Validation and Filtering of Survival Regions in the NBCF Cohort

From the discovery cohort, there were 190 significant probes that mapped to 118 MBDCap regions. In the NBCF Cohort, 60 HM450 probes from 34 of the 118 regions were statistically significantly associated with survival (cox proportional hazards model univariate p value<0.05). To ensure that these 60 probes exhibited biologically relevant levels of differential methylation between survival groups, a subset of the NBCF cohort that exhibited good prognosis (non-event with >5 years follow up, n=18) was identified and a group with poor prognosis (event with <2.5 years survival, n=15) was identified, and the methylation levels across all regions were compared in these two populations. Probes showing <10% difference in median methylation between the good and poor prognosis groups were removed. The validation and filtering process is represented in FIG. 126.

7.2 Results

Following validation and filtering of survival regions, the prognostic regions were limited to a set of 20 loci (Table 14). This final set of prognostic regions contained 39 probes that were statistically significant in the discovery cohort and 35 probes significant in the NBCF cohort. FIGS. 127-146 show methylation levels in the good and poor prognosis groups for each of the probes in the prognostic regions identified in Table 14. FIGS. 147-166 provide Kaplan Meier plots for each of the probes in the prognostic regions based on methylation between survival groups in the NBCF cohort. Detailed performance summaries are provided in Table 15 for the 35 probes showing significant differential methylation between survival groups in the NBCF cohort. The Figures providing Kaplan Meier plots for those 35 probes are also identified in Table 15. All significant probes in the NBCF cohort exhibited an association between increased methylation and poorer prognosis.

TABLE 14 Validated Survival regions Chromosome Start (hg18) End (hg18) Region name FIG. 3 131718242 131718973 chr3-1415 128 7 100732591 100733729 chr7-12819 129 11 125278717 125279989 chr11-24690 130 2 182253383 182254903 chr2-13825 131 7 24290275 24291795 chr7-28113 132 15 50873927 50875994 chr15-2568 133 20 36786620 36791737 chr20-11674 134 11 32404535 32407465 chr11-11623 135 2 104846206 104847327 chr2-26887 136 13 27398788 27401867 chr13-5199 137 13 52321726 52322229 chr13-5196 138 8 23618273 23621219 chr8-13399 139 11 31802820 31804364 chr11-18108 140 11 32410931 32413157 chr11-24196 141 13 27263290 27268058 chr13-13783 142 18 53170434 53170733 chr18-11487 143 2 124498663 124500373 chr2-4092 144 6 27755606 27757375 chr6-25261 145 11 32413697 32415714 chr11-4047 146 2 176653510 176657430 chr2-9331 147

TABLE 15 Summary Statistics of NBCF Cohort Survival Associated Probes hazard Ratio z score p value hazard z score p value start Illumina (uni- (uni- (uni- (multi- (multi- (multi- Chromosome (hg18) end (hg18) Probe ID variate) variate) variate) variate) variate) variate) 2 176654145 176654146 cg01658421 2.272 2.128 0.033 1.977 1.722 0.085 2 176656939 176656940 cg01749491 2.162 2.011 0.044 2.209 2.008 0.045 2 104847180 104847181 cg07112473 2.499 2.390 0.017 2.191 1.938 0.053 2 176657005 176657006 cg07411620 2.301 2.163 0.031 2.201 2.019 0.043 2 176656945 176656946 cg11359133 2.576 2.406 0.016 2.241 1.990 0.047 2 176656842 176656843 cg12074182 2.618 2.438 0.015 2.410 2.173 0.030 2 176656974 176656975 cg14118515 2.730 2.511 0.012 2.777 2.497 0.013 2 124499724 124499725 cg16198087 2.338 2.202 0.028 2.355 2.186 0.029 2 182253807 182253808 cg19711579 2.286 2.122 0.034 1.955 1.615 0.106 2 176656032 176656033 cg23242052 2.372 2.215 0.027 2.289 2.100 0.036 2 176656186 176656187 cg27116912 2.845 2.603 0.009 2.469 2.178 0.029 3 131718727 131718728 cg09685182 2.200 2.024 0.043 1.851 1.544 0.123 6 27757099 27757100 cg03161803 2.567 2.461 0.014 2.202 1.975 0.048 7 24291604 24291605 cg04697148 2.513 2.422 0.015 2.263 2.067 0.039 7 100732831 100732832 cg20713092 2.670 2.556 0.011 2.112 1.691 0.091 8 23619127 23619128 cg01336781 2.345 2.262 0.024 2.171 2.031 0.042 8 23619517 23619518 cg03330710 2.724 2.549 0.011 2.584 2.427 0.015 8 23618863 23618864 cg14451926 2.454 2.355 0.019 2.356 2.206 0.027 8 23619915 23619916 cg15854847 2.474 2.356 0.018 2.529 2.318 0.020 11 32406214 32406215 cg12006284 2.281 2.155 0.031 1.883 1.579 0.114 11 32415633 32415634 cg16325378 2.190 2.048 0.041 1.690 1.250 0.211 11 32411294 32411295 cg16463460 2.267 2.169 0.030 1.817 1.471 0.141 11 31803749 31803750 cg17310258 2.403 2.170 0.030 2.326 2.079 0.038 11 125279521 125279522 cg22097425 2.145 1.991 0.047 1.881 1.587 0.113 11 32415542 32415543 cg24286165 2.483 2.307 0.021 2.033 1.720 0.085 11 32407268 32407269 cg25782229 2.349 2.230 0.026 1.888 1.521 0.128 13 27401235 27401236 cg06883496 2.341 2.246 0.025 2.004 1.770 0.077 13 52322033 52322034 cg10323552 2.351 2.236 0.025 1.990 1.725 0.085 13 52322128 52322129 cg10484958 2.092 1.970 0.049 1.792 1.510 0.131 13 27263587 27263588 cg11444278 2.508 2.347 0.019 2.181 1.954 0.051 13 27267080 27267081 cg20562678 2.160 2.022 0.043 1.699 1.292 0.196 15 50874608 50874609 cg09481404 2.330 2.222 0.026 1.923 1.620 0.105 15 50874360 50874361 cg21038785 2.187 2.074 0.038 1.819 1.522 0.128 18 53170709 53170710 cg24811864 2.632 2.458 0.014 2.190 1.916 0.055 20 36790258 36790259 cg05936441 2.150 2.004 0.045 1.798 1.425 0.154 

The invention claimed is:
 1. A method for indicating to a user whether or not a subject has triple negative breast cancer (TNBC), the method comprising: (a) producing sample methylation data by determining a level of methylation at one or more CpG dinucleotides within one or more genomic regions set forth in Table 1 for a test sample from the subject using one or more techniques selected from the group consisting of nucleic acid amplification, polymerase chain reaction (PCR), methylation specific PCR, bisulfate pyrosequencing, single-strand conformation polymorphism (SSCP) analysis, restriction analysis, microarray technology, next generation methylation sequencing and proteomics; (b) a processor receiving the sample methylation data which is indicative of the level of methylation at one or more CpG dinucleotides within one or more genomic regions set forth in Table 1 for the test sample from the subject; (c) the processor receiving reference level methylation data for the one or more CpG dinucleotides within the one or more genomic regions set forth in Table 1; (d) the processor generating differential methylation data for the one or more CpG dinucleotides within the one or more genomic regions set forth in Table 1 for the test sample from the subject by comparing the sample methylation data to the reference level methylation data; (e) the processor processing the differential methylation data using a univariate and/or multivariate analysis to provide a disease index value; (f) determining by the processor a disease status of the subject based on the disease index value, the disease status being an indication of whether or not the subject has TNBC; and (g) transferring an indication of the disease status of the subject to the user via a communications network.
 2. The method of claim 1, further comprising: (a) determining the methylation data using a remote end station; and (b) transferring the methylation data from the end station to the base station via the communications network.
 3. The method of claim 2, wherein the base station comprises first and second processing systems, and wherein the method comprises: (a) transferring the methylation data to the first processing system; (b) transferring the methylation data to the second processing system; and (c) causing the first processing system to perform the univariate or multivariate analysis function to generate the disease index value based on the differential methylation data.
 4. The method of claim 3, comprising: (a) transferring the results of the univariate or multivariate analysis function to the first processing system; and (b) causing the first processing system to determine the disease status of the subject.
 5. The method of claim 4, wherein the second processing system is coupled to a database adapted to store predetermined reference level methylation data and/or the univariate or multivariate analysis function, and the method comprises: (a) querying the database to obtain at least selected predetermined reference level methylation data or access to the multivariate analysis function from the database; and (b) comparing the selected predetermined reference level methylation data to the sample methylation data for the subject or generating a predicted probability index.
 6. The method of claim 5, wherein the second processing system is coupled to a database, and the method comprises storing the data in the database.
 7. The method of claim 1, comprising: (a) the processor processing the differential methylation data using a univariate and/or multivariate analysis to provide a disease index value, wherein said processing comprises determining that the level of methylation at one or more CpG dinucleotides within one or more genomic regions set forth in Table 1 for the test sample from the subject is decreased relative to the reference level of methylation for the one or more CpG dinucleotides; (b) in response to the processor determining that the level of methylation at one or more CpG dinucleotides within one or more genomic regions set forth in Table 1 for the test sample from the subject is decreased relative to the reference level of methylation for the one or more CpG dinucleotides, the processor making a determination that the subject has TNBC as the disease status; and (c) transferring an indication of the status of the subject to the user via the communications network.
 8. The method of claim 1, comprising: (a) the processor processing the differential methylation data using a univariate and/or multivariate analysis to provide a disease index value, wherein said processing comprises determining that the level of methylation at one or more CpG dinucleotides within one or more genomic regions set forth in Table 1 for the test sample from the subject is increased relative to the reference level of methylation for the one or more CpG dinucleotides; (b) in response to the processor determining that the level of methylation at one or more CpG dinucleotides within one or more genomic regions set forth in Table 1 for the test sample from the subject is increased relative to the reference level of methylation for the one or more CpG dinucleotides, the processor making a determination that the subject has TNBC as the disease status; and (c) transferring an indication of the status of the subject to the user via the communications network.
 9. The method of claim 1, comprising: (a) the processor processing the differential methylation data using a univariate and/or multivariate analysis to provide a disease index value, wherein said processing comprises determining that the level of methylation at one or more CpG dinucleotides within one or more genomic regions set forth in Table 1 for the test sample from the subject is not significantly different to the reference level of methylation for the one or more CpG dinucleotides; (b) in response to the processor determining that the level of methylation at one or more CpG dinucleotides within one or more genomic regions set forth in Table 1 for the test sample from the subject is not significantly different to the reference level of methylation for the one or more CpG dinucleotides, the processor making a determination that the subject does not have TNBC as the disease status; and (c) transferring an indication of the status of the subject to the user via the communications network.
 10. The method of claim 1, comprising: (a) the processor receiving sample methylation data which is indicative of a level of methylation at one or more CpG dinucleotides within one or more genomic regions set forth in Table 2 for the test sample from the subject; (b) the processor receiving reference level methylation data for the one or more CpG dinucleotides within the one or more genomic regions set forth in Table 2; (c) the processor generating differential methylation data for the one or more CpG dinucleotides within the one or more genomic regions set forth in Table 2 for the test sample from the subject by comparing the sample methylation data to the reference level methylation data; (d) the processor processing the differential methylation data using a univariate and/or multivariate analysis to provide a disease index value; (e) determining by the processor a disease status of the subject based on the disease index value, the disease status being an indication of whether or not the subject has TNBC; and (f) transferring an indication of the disease status of the subject to the user via a communications network.
 11. The method of claim 1, wherein the test sample from the subject comprises tissue and/or a body fluid comprising, or suspected of comprising, a breast cancer cell or components of a breast cancer cell.
 12. The method of claim 11, wherein the test sample comprises tissue, a cell and/or an extract thereof taken from a breast or lymph node.
 13. The method of claim 11, wherein the body fluid is selected from the group consisting of whole blood, a fraction of blood such as blood serum or plasma, urine, saliva, breast milk, pleural fluid, sweat, tears and mixtures thereof.
 14. The method of claim 1, wherein the reference level methylation data comprises a level of methylation determined for the one or more CpG dinucleotide sequences within a corresponding genomic region of a sample selected from the group consisting of: (i) a sample from a normal or healthy tissue; (ii) a sample comprising a non-cancerous cell; (iii) a sample comprising a cancerous cell other than a TNBC cell; (iv) an extract of any one of (i) to (iii); (v) a data set comprising levels of methylation for the one or more CpG dinucleotide sequences within the corresponding genomic region in a normal or healthy individual or a population of normal or healthy individuals; (vi) a data set comprising levels of methylation for the one or more CpG dinucleotide sequences within the corresponding genomic region in an individual or a population of individuals having cancer of a non-TNBC subtype; and (vii) a data set comprising levels of methylation for the one or more CpG dinucleotide sequences within the corresponding genomic region in the subject being tested wherein the levels of methylation are determined for a matched sample having normal cells.
 15. The method of claim 14, wherein the likelihood of survival of the subject is a likelihood that the subject will survive at least 3 years after being diagnosed with TNBC.
 16. The method of claim 14, wherein the likelihood of survival of the subject is a likelihood that the subject will survive at least 5 years after being diagnosed with TNBC.
 17. The method of claim 14, wherein the test sample from the subject comprises tissue and/or a body fluid comprising, or suspected of comprising, a breast cancer cell or components of a breast cancer cell.
 18. The method of claim 17, wherein the test sample comprises tissue, a cell and/or an extract thereof taken from a breast or lymph node.
 19. The method of claim 17, wherein the body fluid is selected from the group consisting of whole blood, a fraction of blood such as blood serum or plasma, urine, saliva, breast milk, pleural fluid, sweat, tears and mixtures thereof.
 20. The method of claim 1, wherein the level of methylation at one or more CpG dinucleotides within one or more genomic regions set forth in Table 1 for the test sample from the subject is determined using one or more methods selected from the group consisting of: (i) performing methylation-sensitive endonuclease digestion of DNA from the subject; (ii) treating a nucleic acid from the subject with an amount of a compound that selectively mutates non-methylated cytosine residues in the nucleic acid under conditions sufficient to induce mutagenesis thereof and produce a mutant nucleic acid and amplifying the mutant nucleic acid using at least one primer that selectively hybridizes to the mutant nucleic acid; (iii) treating a nucleic acid from the subject with an amount of a compound that selectively mutates non-methylated cytosine residues in the nucleic acid under conditions sufficient to induce mutagenesis thereof and produce a mutant nucleic acid, hybridizing a nucleic acid probe or primer capable of specifically hybridizing to the mutant nucleic acid and detecting the hybridized probe or primer; (iv) treating a nucleic acid from the subject with an amount of a compound that selectively mutates non-methylated cytosine residues in the nucleic acid under conditions sufficient to induce mutagenesis thereof and produce a mutant nucleic acid, amplifying the mutant nucleic acid with promoter-tagged primers, transcribing the mutant nucleic acid in vitro to produce a transcript, subjecting the transcript to an enzymatic base-specific cleavage, and determining differences in mass and/or size of any cleaved fragments resulting from mutated cysteine residues; and (v) treating a nucleic acid from the subject with an amount of a compound that selectively mutates non-methylated cytosine residues in the nucleic acid under conditions sufficient to induce mutagenesis thereof, thereby producing a mutant nucleic acid, and determining the nucleotide sequence of the mutant nucleic acid.
 21. The method of claim 1, further comprising treating the subject with a chemotherapy drug, or a combination of chemotherapy drugs, effective for treating TNBC if the subject is determined as having TNBC.
 22. A method for indicating to a user whether or not a subject has triple negative breast cancer (TNBC) and treating the subject if they have TNBC, the method comprising: (a) a processor receiving sample methylation data which is indicative of a level of methylation at one or more CpG dinucleotides within one or more genomic regions set forth in Table 1 for a test sample from the subject; (b) the processor receiving reference level methylation data for the one or more CpG dinucleotides within the one or more genomic regions set forth in Table 1; (c) the processor generating differential methylation data for the one or more CpG dinucleotides within the one or more genomic regions set forth in Table 1 for the test sample from the subject by comparing the sample methylation data to the reference level methylation data; (d) the processor processing the differential methylation data using a univariate and/or multivariate analysis to provide a disease index value; (e) determining by the processor a disease status of the subject based on the disease index value, the disease status being an indication of whether or not the subject has TNBC; (f) transferring an indication of the disease status of the subject to the user via a communications network; and (g) treating the subject with a chemotherapy drug, or a combination of chemotherapy drugs, effective for treating TNBC if the subject is determined as having TNBC.
 23. The method of claim 22, wherein the reference level methylation data comprises a level of methylation determined for the one or more CpG dinucleotide sequences within a corresponding genomic region of a sample selected from the group consisting of: (i) a sample from a normal or healthy tissue; (ii) a sample comprising a non-cancerous cell; (iii) a sample comprising a cancerous cell other than a TNBC cell; (iv) an extract of any one of (i) to (iii); (v) a data set comprising levels of methylation for the one or more CpG dinucleotide sequences within the corresponding genomic region in a normal or healthy individual or a population of normal or healthy individuals; (vi) a data set comprising levels of methylation for the one or more CpG dinucleotide sequences within the corresponding genomic region in an individual or a population of individuals having cancer of a non-TNBC subtype; and (vii) a data set comprising levels of methylation for the one or more CpG dinucleotide sequences within the corresponding genomic region in the subject being tested wherein the levels of methylation are determined for a matched sample having normal cells. 